{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trend Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import bt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "returns =  np.random.normal(0.08/12,0.2/np.sqrt(12),12*10)\n",
    "pdf = pd.DataFrame(\n",
    "    np.cumprod(1+returns),\n",
    "    index = pd.date_range(start=\"2008-01-01\",periods=12*10,freq=\"m\"),\n",
    "    columns=['foo']\n",
    ")\n",
    "\n",
    "pdf.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "runMonthlyAlgo = bt.algos.RunMonthly()\n",
    "rebalAlgo = bt.algos.Rebalance()\n",
    "\n",
    "class Signal(bt.Algo):\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    Mostly copied from StatTotalReturn\n",
    "    \n",
    "    Sets temp['Signal'] with total returns over a given period.\n",
    "\n",
    "    Sets the 'Signal' based on the total return of each\n",
    "    over a given lookback period.\n",
    "\n",
    "    Args:\n",
    "        * lookback (DateOffset): lookback period.\n",
    "        * lag (DateOffset): Lag interval. Total return is calculated in\n",
    "            the inteval [now - lookback - lag, now - lag]\n",
    "\n",
    "    Sets:\n",
    "        * stat\n",
    "\n",
    "    Requires:\n",
    "        * selected\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, lookback=pd.DateOffset(months=3),\n",
    "                 lag=pd.DateOffset(days=0)):\n",
    "        super(Signal, self).__init__()\n",
    "        self.lookback = lookback\n",
    "        self.lag = lag\n",
    "\n",
    "    def __call__(self, target):\n",
    "        selected = 'foo'\n",
    "        t0 = target.now - self.lag\n",
    "        \n",
    "        if target.universe[selected].index[0] > t0:\n",
    "            return False\n",
    "        prc = target.universe[selected].loc[t0 - self.lookback:t0]\n",
    "        \n",
    "        \n",
    "        trend = prc.iloc[-1]/prc.iloc[0] - 1\n",
    "        signal = trend > 0.\n",
    "        \n",
    "        if signal:\n",
    "            target.temp['Signal'] = 1.\n",
    "        else:\n",
    "            target.temp['Signal'] = 0.\n",
    "            \n",
    "        return True\n",
    "\n",
    "signalAlgo = Signal(pd.DateOffset(months=12),pd.DateOffset(months=1))\n",
    "    \n",
    "class WeighFromSignal(bt.Algo):\n",
    "\n",
    "    \"\"\"\n",
    "    Sets temp['weights'] from the signal.\n",
    "    Sets:\n",
    "        * weights\n",
    "\n",
    "    Requires:\n",
    "        * selected\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super(WeighFromSignal, self).__init__()\n",
    "\n",
    "    def __call__(self, target):\n",
    "        selected = 'foo'\n",
    "        if target.temp['Signal'] is None:\n",
    "            raise(Exception('No Signal!'))\n",
    "        \n",
    "        target.temp['weights'] = {selected : target.temp['Signal']}\n",
    "        return True\n",
    "    \n",
    "weighFromSignalAlgo = WeighFromSignal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "example1\n",
      "0% [############################# ] 100% | ETA: 00:00:00"
     ]
    }
   ],
   "source": [
    "s = bt.Strategy(\n",
    "    'example1',\n",
    "    [\n",
    "        runMonthlyAlgo,\n",
    "        signalAlgo,\n",
    "        weighFromSignalAlgo,\n",
    "        rebalAlgo\n",
    "    ]\n",
    ")\n",
    "\n",
    "t = bt.Backtest(s, pdf, integer_positions=False, progress_bar=True)\n",
    "res = bt.run(t)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Fpf0VlMcEAOiSLlcWAABWz29/YDDaWR9Of2y23Yvowabkzbm0+uOtPIOtW+eyVnBRBhsA0HNd7o0LAKxet1ZlAFaolfUhG4cNdyE92DRtP5edFZeIPJqN2yW7FzAuAQC6Yl+PbwAYNL/9gcHoct8imNfuBfS6ksF2Pjsrzi5sncs6tlmgXbpUBhsA0G+uMQFg2ATYgMFoBwuc/thszR4PqysRqQfbeaw8g63DO6UvonQpAEBXtCoLmC8DwOB0a1UGYIXUx6dPdloZbCsqESkofS7tDLZV92Dr1rnsIsYlAEBXdHleBgCsntUyems8Hq/7EOiYVgbbyOmPzdYK4qwokNEMDlkwmF0ru3AFZRLbPdi6dS5rZbApEQkA9Fyrz3fH5mUAwOr57T+j0WiUJDmwG3tjHH5Wh58d7OknRY9sj7aylcn5bZxxLq04U8p3ZnbN/1eryGDb7/BO6Z0L6A0IANAVMtgAYNgE2GZ05ZVXJkne9a53rflImNUNN9yQJLnNbW6z5iOhK2Tj0DfNXbKrCGa0AzmmDLNadXbhXod7feyuuP8cAECX6PMNAMO2s+4D2BRXX311brrpprzpTW9Kktz+9rfPaDSSHdUh4/E44/E4N998c975znfmLW95S5Lkjne845qPjK7QT4q+2dnazs3Tcb1/cJAsOdbS5UBOl+20gkyrDnx263PZGo0yyijjjHMwzazcVpIXAOgpfb4BYNgE2GZ0pzvdKe9617vy7ne/O294wxvWfTjM6E53ulOuueaadR8GHdHMptgVLKAHVt7rayyD7TxaGWyrKN057u5mgdFolJ2trVs2NOwfHGR7u1vHCACwDJfGBxln0vt9lJFNRQAwQAJsM9ra2sq9733vvO1tb8s73/nOvPe97814PF73YXHEaDTKzs5Obne72+Waa67JVVddte5DokP0k6Jvdlfc68t35nxWXSJyv+M7pXe3tm8ZO3sHl3LltukmANA/e8qpA8DgWfGYw9bWVu5yl7vkLne5y7oPBTgH2Tj0TTPotZoebN0O5HTV9mjrljKJ4xWUSWwFPju4U3rVmZUAAF3QnCvbjAYAw9S9VRmAFZGNQ9/srjhTSt/C81tlFluXe7Al7WPaW8G4BADogr2Oz8kAgNWzWgYMRnuHodMfm2/lPdj0LTy3dvBzuZ9NsxxoFzcL7LRKl8pgAwD6qXV92cGqAgDA6pkBAINhhyF90yoRqQdbp2yPVtcfr+v9PprHtIrSpQAAXeD6EgDo3qoMwIq0dxi6AGLzNS/kV5LBpm/hua22ROQGZbApEQkA9FRzrqxCCgAMkxkAMBh7YzsM6ZedFWcK7XU8kNNluyss39n13dLNcqIy2ACAvur6nAwAWD0BNmAw9g/sMKRfmpmYy84UOhiPc2msr8R5tfuQLTmDrblbuoOfSyt7Tw82AKCnVEgBALq3KgOwIs1sHDsM6YNVBjJaAenRVkaj0VJfv++2R6vLLuz6uax5THtKRAIAPdX1vrgAwOqZAQCDcTRgAJtulYGMZtaV8pDzawY/Ly0xg208Hnc+G7fdf04GGwDQT+bLAED3VmUAVkSNfPpmZ8B9vrqu+f9smeU7L40PMp7+eWs0ylYHNwu0A78CbABAP5kvAwDdW5UBWBE7DOmblfb5avaU6GCWVNftrKhEZPNz2e1or49V9gYEAOiKrlcVAABWzwwAGAw18umbVZbisyN3Mdut/njLCzLttRZyuvm5NM+vMtgAgL7qel9cAGD1rDADg9HKyOlo5gfMY5Wl+PbHehYuoplddmmJWVzNYF1XNwqsMrMSAKAr9PgGAMwAgMGQkUPfrLIUn+/LYprZhXvj5QU/NyODTQ82AKD/mnM882UAGCYBNmAwWhk5Hc38gHnsriiIkxztwWbBYF6tLK5lZrA1A58d3Snd3MG97NKlAABdYb4MAHRzZQZgBdTIp2/aPdhWmcFmujCvVQWZ9jZgIaedwaZEJADQT+bLAIAZADAYrRr5LoDogd1WltQKM9j0LJxbK/i5xD5km5CJ237vMtgAgH4yXwYAurkyA7ACekrRN80L+WVnCvm+LGZVJSI34XPRgw0AGIJNmJcBAKslwAYMhh2G9M0qM4U2IVOqy1ZVInITen2sKrgIANAl5ssAgBkAMBh7YzsM6ZdVBjL0LFxM67NZYonITdgpvdsILspgAwD6an8D5mUAwGoJsAGD0c78cPpj8zWbqS87kNHqWTjyfZnX6jLYuv+5rCq4CADQJXutCindnJcBAKtlBgAMwsF4nEtjF0D0S7PU6fIz2OzIXUS7fOcSM9jG3c8s1IMNABgCGWwAgBVmYBCOZn2MRqM1Hg0sx2p7sHW/11eXrap8Z+tc1tFM3Na4FGADAHpqz3wZAAavmyszAEumnxR9tLvSHmzNHbmmC/NaVYnITcgslMEGAAyBDDYAwIoZMAjN7B67C+mLZonI5fdgsyN3EasqEdn6XEbd/FxWWboUAKAr9jagsgAAsFpmAMAgyMahj3ZXWCJyEzKlumxVQaZNOJftKhEJAAzAviopADB43VyZAVgy2Tj00ar6fE1er923kPmsqj/eJvTGax7X3hKz9wAAusR8GQAwAwAGobnAvdvRsmowr+aF/N6ye7CN7chdxKr6421CZmH7vctgAwD6aW/c/XkZALBaAmzAIKiPTx+tMpCx7zuzkO1W8HOJGWwbsFN6lYFfAICuUCUFAOjmygzAkqmPTx81A197erB1SvOzubTEMol7G3AuawV+lzwuAQC6YhN64wIAq2UGAAyCDDb6qJkldTAe52A8Xtprt3bkKqs6t1WViGwGrLp6LmsFfpWIBAB6ynwZAOjmygzAkslgo49Go1GrHN8yy0TKYFvMqj6XTTiXbY+2Mpr+eRL4VSYSAOgf82UAQIANGIRWBpvdhfRI82J+mf2u9sfNnhKmC/PabpXvXGaJyGYGWzfPZaPRqHVsy8zgAwDogkvjgxxkUj1ilGRrNDr9BwCAXrJiBgzCJpRVg/PYWVG/K2VVF7PbCORfWmbgs7VZoLufS/PYlhn4BQDoglZ5yK3tjATYAGCQursyA7BEextQVg3Ooxn8WmqvLyVvFtL6XJYa+NyMc9nuigK/AABd0J4rW1oDgKEyCwAGYVOyPmBeuyvr9aWs6iJ2Vla6czMyC5vHtrfEcQkA0AVaEAAAiQAbMBAaUNNXrUDOMjOlxpuRKdVVOysKfG7KuayVwSbABgD0zL65MgAQATZgIJoXQDsugOiRVg+2VfX66nCmVFe1S0QeZDweL+V1W/0+OrxbunlserABAH2jXzEAkAiwAQOxp0Y+PbW7tapMKbtyF7E12spWbm12f7CkANvmZLCtZlwCAHTBvrkyABABNmAgWlkfLoDokVVlCulbuLhWH7Ille9sZ+N293Nply6VwQYA9Iu5MgCQCLABA7EpWR8wr1am0JKCOOPxWFnVJVhF+c5NOZfpwQYA9Fm7RGR352QAwGoJsAGDYIchfdXKFFpSEKcZXNsebWVrNDrl2Zykea5ZVpBpU3rjtbL3BNgAgJ5pzpe7vOkJAFit7q7MACzRngsgemrVQRw9C89vp5VduKwMts04l+2Olp+9BwDQFXsbsukJAFgtswBgEDYl6wPm1SpDuLQgTjPjs7tBnK5bdonIS+ODjDNOkowyynaHs3FX0X8OAKArmnO7XfNlABis7q7MACzRpvQtgnnttkpELiuDbTOypLqulV24hCDT3gZlFurBBgD0mR5sAEAiwAYMRDNgICOHPmmVIVxSIKOZcSTj8/zan83iGWyt81jHF3JW0RsQAKArmpunur7xCQBYHbMAYBBksNFXOyvodSWDbTmW/dls0nlsFYFfAICukMEGACQCbMBANHtTycihT1qBjCX1utK0fTmW/dm0M3G7/bk0e5HIYAMA+qZdWaDb8zIAYHXMAoBB2KTMD5jH7gpK8e37vizFztaQM9j0YAMA+muT5mUAwOoIsAGDsC8jh55aSQ82PQuXoplltozPZn+DeuM1e5HsLSmzEgCgK/T4BgASATZgIFoZOS6A6JFVlOLTtH052iUiZbABAPTF3nhz5mUAwOpYNQMGYa/Vg80FEP2xmh5svi/L0NzNvIwSkZu0U3p3ye8dAKBLVEgBABIBNmAg2j2lnProj90VZArpwbYcu0sOfu5t0HmsudC0J4MNAOiZ5oY082UAGK5ur84ALImMHPqq2edrWSUimwGR5uszn+a5Zhmfzf4GZeK2Ar9LKI8JANAl++bLAECSnWW9UCnl45N8XZKHJdlN8vIkz6i1Xj/Ha3x4kn+b5NFJrkry+iS/lOSbaq1/v6xjBYZHRg59tbOCQMa+HblLsd1YbFlGduGm9mCTwQYA9M0mzcsAgNVZyjabUsp1SV6QSWDsd5K8JMlHJnl+KeWpM77GP0rym0k+McmfJHlektsk+YokLy2lvM8yjhUYnvF43Ao8bNthSI+0erAtKZDRbNqup8T5Nf/fXVpC8HOTen3srmBcAgB0xSZVFgAAVmfh1ZlSyj2S/FCSdyR5ZK31SbXWT8gkwHZDku8tpdzzjNfYSfLD0+P59Frrh9dan5zkfkl+Ocn9k/y7RY8VGKbWxc9oK1uj0RqPBpar3YNtWRlszUCOBYPz2h0tt0TkJvX62FnyewcA6JJN6o0LAKzOMmYBT09yZZLvrrX+0eGdtdaXJXlGJlloZ2WxPTTJ3ZK8stb6PxuvcVOSb57+9aOXcKzAAG1S1gfMqx3IWFIGWzOQM+p2IKfLWtmF48U/m03q9bG75PcOANAlzY1tO+bLADBYy1idecL09jnHPHZ43xPPeI3Dmcldp9lsTXeZ3r5t7iMDiPr49NuygziJDLZl2W6VSVxCBtt4c85lerABAH3mGhMASJKjway5lFJGSR6cSYDs1cc85U+mjz2klDKqtY5PeKk/TvL6JPdO8pOllH+b5G+SfESSZ05f4z8tcqzAcNldSJ+tokRkO5DT7UypLmtm/y3js2mdyzq+kLOKcQkA0BX7ehYDAFk8g+2OmZSHfGut9eajD9Za95O8Jcntklx90ovUWveSfEaSv07y2Un+LMm7kvxakiuSPKHW+ssLHiswUOrj02etDLYlZQptUiCny5adXbhJ57Lme5fBBgD0zSb1xgUAVmehDLYkt5/evvuU57xnentVkhtOed6fJfmpJF+Z5OVJ3pzkEUnukeSrSykvr7WeWSaylHJdkuvOet7UtTM+D9hg+2PBAvprt9WDbTmZQvsbFMjpsp0lZ3FtUjbusrP3AAC6RJ9vACBZPMB2OKM4qfRj04kzjlLKnZO8OMk9k3x8rfVF0/uvTPL9Sb4oyS8meewM/859ZnweMBB7+knRY6voweY7sxw7o+VmF25Srw892ACAPtukeRkAsDqLBtjeNb297SnPOXzsxlOe86+TPDDJVx8G15Kk1vreUso/S/JRST66lPJRtdbfPOOYXpfkN854zqFrk9xhxucCG0o2Dn3WDmQsK4OtUfKm45lSXdYOfi4hg22Den3sriDwCwDQFZtUWQAAWJ1FA2w3ZBJku0spZWfac+0WpZSdJHdJclOt9e2nvM7HTG9fcPSBWuteKeXXkpQkD09yaoCt1vqsJM+a5eBLKS+KbDfovT0XP/TYbqsM4ZIy2DYokNNlO0suk7i/QTulVxH4BQDoiuZ8uevzMgBgdRZaNau1jpP8cZLtJA845ill+m/84Rkv9Q+mt/snPH54/xVzHiJAK3tCBht9sz1qZ0mNx7NUbT7dvqbtS7Hs8p3tzQLdPpc1j+/S+CAHSxiXAABd0cpgc40JAIO1jFnA9dPbJx/z2OF9zzvjNV4zvX3S0QdKKdtJPnb611fOeWwA+knRa1uj0WVBtkX5zixHuwfbsDLYRqPR0nvQAQB0wcF4nEvjzdn4BACszjJmAT+W5KYkX1NKecThnaWURyb56iTvSfLMxv33K6U8sJTS7H32X6a3X19K+cjGc3eSfEeSD84kU+7Xl3C8wMDIxqHvWplSyw7kWDA4t50ll+/cay7kbMC5rPX+lxD4BQDogua8bme0ldFotMajAQDWaeFVs1rr65J8ZZJrkryklHJ9KeX6JL+V5OokT621vrnxIy9M8uokn9p4jecl+Q/T1/jNUspvlVKek+TPk/zLJH+b5DNrrbY/A3OTjUPf7Y6WHMg52KxATle1S0QOK4MtaR/jngw2AKAn9mzgBACmlrItvdb6zCSfnOS3kzwmyaOSvDjJ42utz57xNb52+hq/luRBmZSLPEjy/UkeXmt91TKOFRieVgabbBx6qBnI2VtCry99C5djpxX4XHbpzu5/LsvOrAQA6ILmXNlmNAAYtp1lvVCt9blJnjvD8+6z6GsAzGPPBRA91y5FqAdbV+y2MtiWEPjcsN3Su0sukQkA0AV7BzajAQATZgJA7+1vWNYHzKuZmbmMQMamBXK6anuFvfF2NiAbt3mMy8isBADogn3l1AGAqe6vzgAsSI18+q6VwbaEXl+tDLaR78x5Lb1E5Hhze7ApEQkA9EUrg81cGQAGTYAN6L19wQJ6rtWDbRkZbONmUNpU4bxWWSJyE3ZLN49xGeMSAKAL2j3YzJUBYMjMBIDea2d9OO3RP8vMFBqPx0d6sPnOnNcqe+Ntwrlsd8klMgEAukCFFADgUPdXZwAWtGlZHzCvVq+rBTOFLjWy17ZGo2xtQK+vrtpu9sYbH2Q8Hi/0eq1z2QZk4zaPUQYbANAXenwDAIfMBIDe2z/YrL5FMK/dJfZgsyN3eSYBytEtf7+08GezWeey9rgUYAMA+mHT5mQAwOoIsAG9p9wdfbfMXld6Fi5Xs/H9ImUSD8YHOcgkA26UtAJ3XbXs3oAAAF2waVUFAIDVsdIM9F7zAmjXBRA9tNPqdbVYIEPPwuVqfTYLZHEdLXU72oAA2zJ7AwIAdIX5MgBwyEwA6L3mBZAMNvpoZ0lZUkd/Xs/CxW23+uOd/7NplyLajPPYMnsDAgB0hfkyAHBoM1ZoABawr6cUPbe7pCypRE+JZWv+P1ykB1uzt96mlCJaZm9AAICuMF8GAA4JsAG91+7B5gKI/mn3YFtiBtvINGFRyyrfuYkLOcvsDQgA0BXtnsXmywAwZGYCQO/tb2BpNZjHUnuwbWAgp8ua2WaLBD/3N3CjwO4SxyUAQFfsqZACAExZaQZ6b+9g80qrwTyW2oNNz8Kl2l5S+c72Qs5mfC7LzKwEAOgK82UA4JCZANB7zQugTVmYhnk0x/XeEnuwbUqmVJe1s7iWlMG2IaWImscpgw0A6AvzZQDg0Gas0AAswAUQfdcc14sGMvaVvFmqZWUXbuJ5bFcPNgCgh8yXAYBDAmxA77kAou92l1gicm8DM6W6bGdJJSL3x5t3HmsFfsdKRAIA/bCJlQUAgNUwEwB6rx0w2IyFaZjHTqtE5KI92DYvkNNl7ezCZWWwbcb0rVW6VAYbANATe+bLAMDUZqzQACygHTBw2qN/WllSCwYyNjGQ02XL6kPWysTdkI0CyyqPCQDQJfvmywDAlJkA0Gvj8VjAgN5bZq+r5oKBHbmLa5eI1IMNAGDT7ZkvAwBTVpqBXrvUWNDeGo2ypUY+PbTMTKG9xs8rqbq4ZX02zf5tm5KJu6z+cwAAXbKvBQEAMLUZKzQA59QMFthdSF8tM5DRzmAzTVjUsj6bzc9gUyISAOgH15gAwCErZ0Cv2V3IEDQDLgtnsI03L5DTZe0ebAtksDUzCzck8LnM3oAAAF3RLPu9KfMyAGA1zASAXtvbwLJqMK/m2F68B5sducvUCn4uqQfbpnwuerABAH20ifMyAGA1rDYDvdbO+nDxQz8ttwdbM+vTNGFRy8ri2t/A3nitcblAcBEAoEtaVVJs4gSAQTMTAHrN7kKGYHepPdhksC3TsoKf7WzczfhcZLABAH3U6lm8IRufAIDVEGADeq2d9eGURz8ttQebHblL1fx/uLdA8HMTd0q3s/dksAEA/bA3ViUFAJjYjBUagHOSwcYQLDNTaH8DM6W6rBnYv7RIBtsGZhbKYAMA+qiVwbYhG58AgNUwEwB6rRks2JSsD5hXM4izaInIdgbbZgRyuqyVXbhAH7L9DeyN1xqXAmwAQE/s6fMNAExtxgoNwDnJYGMIdlqZQouV4mv3YDNNWFSrP94CQaZNPJcdDS6Ox+M1Hg0AwHLsb+C8DABYDStnQK/t213IABztdbVIIKPdt9B3ZlHbjSyuRYKf+xvY62NrNGq9/0Uy+AAAumA8HrfmNNsbUlkAAFgNMwGg11pZHy5+6Knt0Va2MkqSjDPOwQIBtj092JZqWSUi9za018eyMvgAALqgtelptJWt0WiNRwMArNvmrNAAnMMmZn3AeTSz2PYW6MPW6vW1QYGcrmoGmC4tEGDa1MzC5rEuWr4UAGDdzJUBgCazAaDXNrFvEZxHK1NqgUDGXqsHm+/MopYVYNrUc9lua1zKYAMANtumzskAgNUQYAN6zQ5DhmJntJxSfK3vzAZlSnVVqz/eAiUi98ebeS5bVmYlAEAXbGpVAQBgNTZnhQbgHGTjMBTN8b28TCnThEUtK/C5qeey3SVlVgIAdIG5MgDQZDYA9Fo7G8cpj/5qlYhcpAebvoVL1fx/eGmRDLYNzcZtZbApEQkAbDhzZQCgaXNWaADOoVmSbJOyPmBezR20i/Vg851ZpnaAaXifix5sAECf7LU2PW3OnAwAWA0BNqDXWjXyXQDRY8soRTgej498Z0wTFtXszbFYb7zN7PfRPNZFAowAAF2wr0QkANBgNgD0mhr5DMVOqwfb+QI5B+NxxhknSbYyyrayqgtrBin3FygRuanZuK3MygVKlwIAdMHehm56AgBWw8oZ0GubmvUB82qV4jtnIKcZxJG9thztzMJFerBtZmZhO/Argw0A2Gz7Yxs4AYBbmQ0Avba/oVkfMK92r6/zZQrtb2ifry7baQU+z59ZeGnc3CywOdM3PdgAgD7Rgw0AaNqcFRqAc2hfADnl0V/tXl/nzGDTs3DpWiUiz/m5NANTO6OtjEajhY/rojSDgecN/AIAdEVzPmdDGgBgtRnoNRdADEWzRM3eOTOlNG1fvmYfu0vjgxyMx3O/xt4Gn8eWUboUAKArZLABAE1Wz4Bek8HGULRKES4jg03PwqXYGo0uC7LNa3+8uQs5yyhdCgDQFa0NnBtUthsAWA2zAaDX2hdAm7UwDfNoluI7b68rPQtXY9EykXsbnFm4jMAvAEBX7G3wxicAYPk2a5UGYE4ugBiKnSWU4pPxuRrt/njzBz/3N7g3XnNjgww2AGDT7ZsvAwANZgNAr7V7Sm3WwjTMozm+zxvI0LNwNVoZbOcIfrYy2DYsE7cd+BVgAwA2257rSwCgQYAN6LVWTyk7DOmxdhnC8wUyWhlsekoszaLlO9s92Dbrc9nVgw0A6JF9PYsBgIbNWqUBmJMMNoaiXYbwfCUifV9WY9HynXsbnFmoBxsA0Ceb3BsXAFg+swGg12TkMBStTKFzluLbG29ur68u221lF84fZNrkXh+7S8isBADoin3zZQCgYbNWaQDm1LwA2rTMD5jHMjKF9u3IXYlFS0Rucq+PnVZvQBlsAMBm2+R5GQCwfFbPgF5rZbC5AKLHlpEp1O5Z6PuyLNvNINM5SkRucq+P3VbpUhlsAMBm2+TKAgDA8pkNAL01Ho/bC9MugOixZuDlvJlCrQWDDQvkdFkzg+3SeUpEjjc3s3BnCaVLAQC6otUb13wZAAZvs1ZpAOZwMB5nnHGSZCujbOvBRo81Axn75+7BtrmBnC7bXfCz2eTMwt0llC4FAOiK5lzOBk4AwGwA6K12sGCzFqVhXrutXlfnC7DtK6m6Eov2Idvf4F4fO0sYlwAAXaEHGwDQJMAG9Jb6+AxJK4PtnJlCrZI3vjNL08yePU8fslYvyQ3LxN1d8L0DAHTJ/gZXFgAAlm+zVmkA5tAOFrj4od+aPdPOG2CTwbYazWDlpfF5erBt7rmsOY72z/HeAQC6pJ3BZkkNAIbObADoLRlsDMlSerBp2r4Si5aI3Nvgc1lz4UmJSABg07Uy2MyXAWDwNmuVBmAOggUMyVJ6sI1lsK3CzoJlEvc3OBu3lcF2zsxKAICu0OcbAGgSYAN6S7CAIVl2iUglb5Znu5VduGgG22ady5YR+AUA6ApVUgCAJrMBoLc2uawazGt3ySUiNy2Q02W7reDneTLYGoHPDcvGbQV+zzkuAQC6Qp9vAKDJijPQW5tcVg3mtYxSfO1AjinCsuwsmsE2bgY+N+tzafdgUyISANhsrQy2Ddv4BAAs32at0gDMYZPLqsG82oGM82aw+c6swqLBz3bpzs36XFrBxYNLGY/HazwaAIDF7CmpDgA0mA0AvSUbhyFZSg+2sazPVdgZtYNM89rkcrdbo61sjUZJknGSS+fI4AMA6ILxeNyaL9uQBgBs1ioNwBz2XPwwIM3Ay965e7BtbiCnyxYtEdkMmG5iKaLdJQR/AQDWrTmP2xqNbtlEBAAMl9UzoLf2le9gQLYbWVIH43EOzlGKT9/C1Vg0u3Bvg0tEJu0NDuctXwoAsG7mygDAUVacgd7aO5DBxnCMRqPWhf7CpQiVVV2adgbb/J9LuxTR5n0uuwtm8AEAdEF7ruz6EgAQYAN6bN8FEAPTDIrtnSNTqhn8sSt3edo92GSwAQBsovZc2XIaACDABvTYngsgBqYZyDhPppSsz9XYWTCzcH/De+PttgKMAmwAwGZq9ys2VwYABNiAHtt3AcTAtEoRnieDTd/CldhZsERi63PZwGzcdgabEpEAwGbSgw0AOMrqGdBbe60LIKc7+m+5PdgsGixL8//luUpEjjc7s3DRHnQAAF2gXzEAcJQZAdBbMtgYmlYPtnMEMuzKXY3dBQNMm55ZuKsHGwDQA+bKAMBRO8t6oVLKxyf5uiQPS7Kb5OVJnlFrvX6O17gqyb9O8plJ7pvk3UlenOSbaq2/u6xjBYahlcEmG4cBaPf6mi9T6tL4IAcZJ0lGSbZGo2Ue2qBtN4Ji5ymRuOm98RYZlwAAXbG34X1xAYDlW8qMoJRyXZIXJHl0kt9J8pIkH5nk+aWUp874GndK8n+T/LskVyd5XpLXJ/nkJC8upTxqGccKDEczU2QTF6VhXq1MqTkzhfaPBHFGAmxL0ywReWnh3nibdy6TwQYA9EHz+nIT52QAwPItHGArpdwjyQ8leUeSR9Zan1Rr/YRMAmw3JPneUso9Z3ip707y0CQ/m+QDaq2fVmt9WCYZbVcm+ZFFjxUYlk0vqwbzagZy5s2U2vN9WZlFSkSOx+PsN3qwbW9gv49m6dLz9AYEAOiCoxvSAACWsUrz9EwCYN9da/2jwztrrS9L8owkt0lyahZbKeX9kjwlyV8kua7WenPjdb4zk3KTty+l3GUJxwsMxKaXVYN5LRLIafUsVFJ1qRYpkdgMru2MtjaydGcrg22sRCQAsJlaG9I2cNMTALB8y+jB9oTp7XOOeew5Sb41yROTfMMpr/FpmbR8+YFa63uPPlhrfeRihwgM0b4LIAZmZ2uRDDZN21elmXU2b4nE/R70+thZoHQpAEBXtDY+mS8DAFkwwFZKGSV5cJKDJK8+5il/Mn3sIaWUUa11fMJLfcj09qWllKuSfHaSRyTZT/LCJP/rlJ8FOFa7CbULIPpvkVJ8ehauTjuzcJHSnZv5uejBBgD0QR/mZQDAci26FfqOmZSHfGuzrOOhWut+krckuV2Sq095nftPb++S5I+S/NckX5pJ+clfTPKCUsppPw9wmeZCtgsghqA5zhcL5GxmplRXNUtuXpq3RGSz1O2Glu5sHve8JTIBALqiD5UFAIDlWrRE5O2nt+8+5Tnvmd5eleSGE55zh+ntjyX58ySfk0mg7aFJnpnk45L8cJLPPeuASinXJbnurOdNXTvj84ANtOcCiIHZWSBTSNP21Wmef/bm7I3Xh8Bn87hlsAEAm0pJdQDgqEUDbIerJLOUbzxtVejK6e3NST6+1vr26d//bynlEzIpNfnZpZRvrLX+yRn/zn2SPHaG4wF6bt8FEAOzSK+rVkBaz8KlavZgOxiPczAeZ2s0muln+9DrY2eBzEoAgK7YN18GAI5YNMD2runtbU95zuFjN57ynMMMuJ9uBNeSJLXWN5VSfinJ5yX5mEyCbad5XZLfOOM5h67NrdlzQM+0AwabuTAN81ikFF+zB5uA9HKNRqPsjLZuCS7tH1zKFduzTcH60EtSDzYAoA/2zJcBgCMWDbDdkEmQ7S6llJ1pz7VblFJ2MumrdtPRwNkRfze9fd0Jj//l9PYuZx1QrfVZSZ511vOmx/eiyHaD3moHDOwwpP+a43x/7lKEzUwp35dl29nayv6lyf/jS3Nkce33oERkc4f3vJmVAABdoaQ6AHDUQis1tdZxkj9Osp3kAcc8pUz/jT8846UOH7/HCY/ffXr75nmPERguF0AMTbsH25wZbAd25K5SM7twns+mFfjc0EzcXSUiAYAe6ENvXABguZYxI7h+evvkYx47vO95Z7zG86e3nzrNertFKeWKJI+b/vU3z3F8wEC5AGJolteDbTMDOV22c87swj5k4jbfuxKRAMCm2u/BxicAYLmWsVLzY0luSvI1pZRHHN5ZSnlkkq9O8p4kz2zcf79SygNLKc3eZ7+W5JVJPjDJ95RStqfP3UrynUnum+QFtda6hOMFBkIGG0Oze84sqaT9fZHBtnzt4Oc8GWz96sE2b29AAICu2FPxAQA4YuEAW631dUm+Msk1SV5SSrm+lHJ9kt9KcnWSp9Zam6UdX5jk1Uk+tfEal5J8TpI3JfnnSf68lPKLSf4kyZcleX2Spy56rMCwtC6A7DBkAHZapfjmyxRqPl8PtuVr7nKeJ8jUh8Bnu3SpDDYAYDOZLwMARy1lRlBrfWaST07y20kek+RRSV6c5PG11mfP+BqvTnJtku+b3vWkJLtJfiDJh04DeQAzuTQ+yEHGSZJRRtkajdZ8RLB6uwuViNz8QE6XnbdEZD8y2M4/LgEAukIGGwBw1M7ZT5lNrfW5SZ47w/Puc8pjf5vky6f/AZxbuzzkVkYCbAzAzqjZ62reEpF25K7SzjnLJLYy2Eab+bnsLFC6FACgK45eYwIAmBEAvbRvdyED1C4ROV8gQ0nV1WoGP+fJ4tob9yGD7fylSwEAusJ8GQA4SoAN6KVWWbUNzfqAebUCGXOW4msG5DY1kNNl7RKR82SwbX5mYfO49WADADaV+TIAcNRmrtQAnKF58SODjaFYJJDR7ilherBszTKJ85SI7EOvj91zlscEAOgSVVIAgKOsoAG91Mpgc/HDQJw3iHP0+b4zy9fOYJs9+Nn6XDa0FFG7B5sMNgBgM+3pwQYAHGFGAPRSc1FaNg5Dcd4gTtKPTKkua/dgO28G22aey3bPWR4TAKBLZLABAEdt5koNwBlksDFEi5Ti29e3cKWa56F5srj60OvjvO8dAKBL9PkGAI4yIwB6qbW7cEPLqsG8FurBpm/hSjU/m0tzZHH1IbOwHfgVYAMANlOzQsSmzssAgOUSYAN6aW/czGBzqmMYdhfqweY7s0rn7Y/Xh8+lHfhVIhIA2Ex7ehYDAEds5koNwBnaPdhc/DAMerB1VyvINMdn01zI2dRs3O3RVrYySpKMM54rgw8AoAvG43EvNj4BAMtlRgD00p6LHwao3etq3gy2xo7cDQ3kdFkr+DlPBltPsnEXKV8KALBuB+NxxtM/b2WUbT3YAIAIsAE9JYONIVqk15UMttVqBi0vzVUish/nsvbYlMEGAGwWc2UA4DgCbEAvtTLYZOMwEM2dtPvjg4zH41Oe3bY/bvaUMD1Ytt1zl4hsZrBt7rmsnV0pgw0A2Cx9qSoAACyXWQHQS80LoF0XQAzE1mh0WZBtVnblrlbrc5krg60f57Ldc5bIBADogr2eVBUAAJZrc1dqAE7RvADa5KwPmNe5e33pW7hSO+cs37nXk954zWOXwQYAbBpzZQDgOGYFQC/ty8ZhoHbPGcho7crd4EBOVzUzuC7NkVnYzsbd3M+l1YNtjhKZAABd0Kr2YK4MAEwJsAG91O7B5lTHcLQy2OYIZLT7Slg0WLZ2Btf5Sndu8m7p5rHLYAMANk27X7G5MgAwsbkrNQCnaF4AbXLWB8yrXYrwvD3YTA+W7dyBz570+9g957gEAOiCvmx6AgCWy6wA6CUXQAzV7qjZg222QM7B+CAH4/Etf9+W9bl0O6Pz9cZrZ+NuboCt+f5lsAEAm6Yvm54AgOWyggb0kgsghqqZwTZrKcKj35fRaLT04xq6dmbhPKU7m5/N5k7b2j3YZLABAJulvYHT9SUAMLG5KzUAp3ABxFCdpxRhMxCnZ+FqtD+X2QJM4/G4N+eyduBXBhsAsFmaG6R2zZcBgCmzAqCXXAAxVOfpddUMxMn4XI1mecdZP5dLjUDc9mgrWxucWdjMvpsngw8AoAv6sukJAFguq85AL+01FqZdADEk5+l1pWfh6i2cWbjhn8t5SpcCAHRFX8p2AwDLZVYA9FIrg02AjQHZPUevL9+X1WsF2GbujdfMxN3sz6WdwSeDDQDYLDLYAIDjCLABvSQjh6FqZQrN2Our3YPNgsEqtAJMs34u4/6cx5o7vfdmzOADAOiKffNlAOAYm71aA3CC5gWQjByGZOccva7aGWymBqtwvs+lP+exnXP0BgQA6Io9FR8AgGNYRQN6qZXBZochA9IuxTdrppSehavW7I038+fSo1JErQw2JSIBgA2z36PKAgDA8pgVAL2kCTVDdZ5Ahh5sq9fK4JqxRGI7g22zz2My2ACATbbXo8oCAMDybPZqDcAJ+pT5AfNoB3LOkyllarAK7RKR5/hcNjwTd7dx/DLYAIBNs2++DAAcw6wA6CUXQAzVor2+Nj2Q01W75yjd2adSRK1xOWMGHwBAV7R6sJkvAwBTm71aA3CCVgkPF0AMyPl6sDVLRJoarMLWaHTLnw8yzsEM2YV7PSrdudsqESnABgBsln09iwGAY1hFA3qpnfnhAojhaAYy9mbu9eX7smqj0Sg7o/nKRLYyCzf8c2m+9z092ACADdPe+GQpDQCYMCsAeskFEEN1nhKRmrZfjHn747VLEW32eUwGGwCwyfq08QkAWJ7NXq0BOMbB+CAH4/Etf9/e8IVpmMe5en01M9h8X1Zmd87gZ59KEe20MitlsAEAm6VPG58AgOUxKwB6Z/9INs6o0fsI+q6ZwTZrIEMG28WYtz+eHmwAAN2gpDoAcBwBNqB3msEC2TgMzXkCGe0FA9+ZVdluBT9nyGDr0efSCvwKsAEAG8aGNADgOJu9WgNwjP1xf7I+YF7nCWTs+c5ciGY5odky2PqzkNMO/CoRCQBsluY15qZvfAIAlsesAOidvR5lfcC85i1DOHmekjcXofn/9tIM5Tv71BtvZzRf/zkAgC452oYAACARYAN6yMUPQ9YMKu/PUIYwOfKdGfnOrMq82YV9yixsHv+svQEBALqitYnTfBkAmBJgA3rHxQ9D1gpkzJjBJuvzYmzPWSKy+ZxNzyzcOUdvQACArmi3ITBfBgAmzAqA3nHxw5C1MthmDGTsj2V9XoRWH7IZsgubgc9NP5fttrL3ZLABAJtlr0cbnwCA5dns1RqAY7j4YcjO04NNBtvFaAc/58xg2/Bs3Pa4lMEGAGyW/R5tfAIAlsesAOid9sXPZi9Kw7x2z9WDzXfmIswb/Nzr0efS7sEmwAYAbJb2hrTNnpcBAMsjwAb0jmwchmyn1YNttkDGXo8ypbpsZ87gZ/M5m34u2xqNMpr++WA8zsFYmUgAYHM0N0ftmi8DAFObvVoDcAwXPwxZq8/XjCUi9S28GDuj+UpE9imDbTQatYK/s45NAIB1uzQ+yEHGSZJRRtkajc74CQBgKKyiAb2z16OsD5hXK4gzYyk+JW8uRivANEMGV6sHWw/OZc3g7azZlQAA63Z0TjYSYAMApjZ/tQbgiFYGm2ABA9MuETljBpvvzIVolYicIcDUymDrQTZuqwedEpEAwIboU1UBAGC5BNiA3pGNw5AdDeKMx+Mzf6b1nRmZGqzKvCUim0GoPpzLds/RHxAAYN32zZUBgBOYGQC907oA6kFZNZjH9mgrW5mUrRknOZghwCaD7WK0S0SeHWDa79lu6XYPNgE2AGAz7JkrAwAnsPIM9E7rAqgHZdVgXs3A8t4MgZw9QekLMW8G216ve7ApEQkAbIb9sQopAMDxNn+1BuAIF0AMXTtTaJZShP3KlOqqdgbbDJ+LDDYAgLVr92CzjAYA3MrMAOgdF0AM3by9rtqZUpsfyOmqo/3xztK33njN96AHGwCwKfbNlQGAE2z+ag3AES6AGLp5ShEejMe51Mim6kMgp6vmLRHZt8zC3Tkz+AAAuqC1gVMLAgCgwSoa0Dv7LoAYuHYpwtMzhY4G10aj0cqOa+ian8veDAGmvmUWtnoDymADADZEuwWBZTQA4FZmBkDvNBeuXQAxRLtbs2dKtcoQ9iCI02XN89GlMwJM4/G4tVmgD+ey3Tl7AwIAdEFz01MfqgoAAMuz+as1AEe0MthcADFArQy2MwI5+3oWXphmRu0spTvH0z9vZZTtHpTu3BnN1xsQAKAL+rbpCQBYHjMDoHdk5DB0zV5fZwUy+laGsMu2myUSzyjdudez/mvJ0R5sAmwAwGbYs4ETADiBABvQOzJyGLp2IOP0TKl2z0Lfl1VqBj7PymDr405pPdgAgE3UnLft6PENADT0Y8UGoGHPBRADN08gY28s4/OiNAOfl84IfPax14cebADAJtqzgRMAOIGZAdA7+2MXQAzbPL2+9pWIvDDzBD5lsAEAdEOzIoT5MgDQ1I8VG4AGPaUYup15en3pKXFhtucoEdnKYOtJJu48gV8AgK4wXwYATiLABvTOvgsgBm5njlJ87Z4SpgWrNFdvvB6W7my+j7MCvwAAXdGqLGC+DAA0mBkAvdPH0mowj51WppQMtq5ofi6X5vhc+nIea5bsPWtcAgB0RR974wIAy9GPFRuAhj6WVoN5nD9TyrRgldoZXLNnFvZlIaf1/pWIBAA2hPkyAHASMwOgd/pYWg3m0Q5kyGDrip2teXqw9e881gr8ymADADaE+TIAcBIBNqB32hdATnMMz84cpfhaPdgsGKzUPKU7W70ke9Lro/n+zwr8AgB0hfkyAHCSfqzYADS4AGLodkbNTKHZM6X6Esjpqp05Snc2S0j25Tw2T+lSAICuMF8GAE5iZgD0ysF43Fq43XEBxAA1Mzf3xmdkSvUwkNNV85SI3O9hJm7z/ctgAwA2hfkyAHCSfqzYAExdOhJcG41GazwaWI9WptQ8GWwWDFZqe7SVUSbnpHHGrfPVUXs9zMTdnWNcAgB0hfkyAHASATagV5oXP31ZlIZ57c7Vg635nTEtWLVZs9han8uoH+ey5vuQwQYAbArzZQDgJGYGQK/s210IRwIZZ2Ww3fq478zqNcvWnhb8bJb27Mvn0u7BJsAGAGwGGWwAwEkE2IBeaZdVc4pjmOYJZLQzpXxnVq1VvvOUEpF93Cnd7sGmRCQAsBmaVQf6UlkAAFiOfqzYAEzJYIOjgYzTA2x25F6sWUtE9jGzsN2DTQYbALAZ2teYltEAgFvtLOuFSikfn+TrkjwsyW6Slyd5Rq31+gVe8/lJnpDkcbXWFy3jOIF+a5ZVk43DUM0axEnaWVT6Fq7erCUi+5/BJsAGAGyGPfNlAOAES1mxKaVcl+QFSR6d5HeSvCTJRyZ5finlqed8zadlElwDmNl+D7M+YF7N0jVnBdj27Mi9ULOWiGxlsPWkFNHuHOMSAKArVEkBAE6y8EpaKeUeSX4oyTuSPLLW+qRa6ydkEmC7Icn3llLuOedr3j/Jdyx6bMDw7LWyPlz8MEzz9WDTU+IitTPYTunBNu7fuaz5PvbOGJcAAF3R6vOtSgoA0LCMmcHTk1yZ5LtrrX90eGet9WVJnpHkNklmzmIrpWwn+fEkNyf5ozOeDtDSzmBz8cMw6cHWXa3ynacEmfrY62N3jtKlAABdIYMNADjJMlZsDss4PueYxw7ve+Icr/fVmZSafHqSvz33UQGD1Mpgk43DQM1TIrKdKdWPQE6XzfrZtHZK92QhZ7ux4/vS+CAH4/EajwYAYDZ75ssAwAkWmhmUUkZJHpzkIMmrj3nKn0wfe8j0uWe93sOSfGOSX6i1/vQixwYMUzNYYHchQ7U7Y5ZUcqTXl+/Mys2aXdjHndKj0ahdvvSM7EoAgHU7aGwKGqW9YQgAYNGZwR0zKQ/51lrrzUcfrLXuJ3lLktslufq0FyqlXJHkJ5O8PcmXLnhcwEC1+knZXchA7WzNkcF2YEfuRWr+P740Pi2DrZmN25/PpdWD7pT3DwDQBftHqgqMRmfuHQcABmRnwZ+//fT23ac85z3T26uS3HDK8745yQcn+dRa61vOe0CllOuSXDfj0689778DdFNrUbonWR8wr91z9mBTVnX1dmcsEdkMPvUlgy2Znpcv7SU5e2wCAKxbXzc9AQDLsWiA7XCmMUsTjRNnIqWUj0zyVUmeXWt9zoLHdJ8kj13wNYAN1Vyw3nUBxEDN14Otn4GcrtpuBj9PKd+519PMwlb50jPGJgDAupkrAwCnWTTA9q7p7W1Pec7hYzce92Ap5fZJfjzJ3yT5sgWPJ0lel+Q3ZnzutUnusIR/E+iIdgNqF0AM086MQZykv4Gcrpq1fOd+T3vjNd+/DDYAoOvMlQGA0ywaYLshkyDbXUopO9Oea7copewkuUuSm2qtbz/hNZ6W5H5J/iDJ95dSmo89ZHr79aWUL0ryw7XW3zztgGqtz0ryrFkOvpTyosh2g15p9pPq06I0zGN7tJVRJunlB+NxDsYH2Toho7OvgZyuambW7p8SYOpr6c5Wicwzgr8AAOvm+hIAOM1CAbZa67iU8sdJPjTJA5K86shTSialIf/wlJe5anr70Ol/x/n46e2vJTk1wAYMmx2GkIxGo+xsbd/yfdg/OMgV28d/H/oayOmq5nmpWXLoqGbwabdH57KdOfoDAgCs215jM5q5MgBw1KIZbElyfSYBtifn8gDbk6e3zzvph2ut35jkG497rJTya0k+Lsnjaq0vWugogUGQjQMTO6Ot7E1bpe4dHOSKE74O7V25/QnkdNWsJSJbizk9Opftzvj+AQC6wFwZADjNMmYHP5bkpiRfU0p5xOGdpZRHJvnqJO9J8szG/fcrpTywlKL3GbB07WwcF0AMVyuQc0IpvvF43Mqi6lMgp6u2ZywR2dfFHBlsAMAmaVdIMVcGANoWXrGptb4uyVcmuSbJS0op15dSrk/yW0muTvLUWuubGz/ywiSvTvKpi/7bAEc1gwUy2BiyZlDmpEyh5vdle7SVrdFo5cc1dLszlojs62KODDYAYJO4vgQATrOULdG11mcm+eQkv53kMUkeleTFSR5fa332Mv4NgFn0dVEa5tUc/ydlCu3rWXjhmr07Tgswtcrd9qjfR/P9y2ADALpOj28A4DTL6MGWJKm1PjfJc2d43n3meM2PX+SYgOHpa1k1mFezROpJJSL3ehrE6bKdrbM/l0vjgxxknCQZZdSrzMLdGd4/AEBX9HXTEwCwHFafgV5pBgx2XAAxYDszlOKTwXbxZslga96/s7WVUY8CbO3MSiUiAYBu2xubLwMAJzM7AHqlmRGhRj5D1u7BdkIGm+/LhduZ5XM56O/n0u7BJoMNAOi2/R7PywCAxQmwAb2iRj5MtHtdzZIpZcHgIrRLRM6QWTjq13ms+X70YAMAum7PfBkAOEW/Vm2AwWvVyHcBxIDN0utqT8/CCzdLici9Hp/HWhlsJwQYAQC6Qkl1AOA0ZgdAr8hgg4lZel21M6X6Fcjpqp0ZAp/7rV4f/fpcmu9fBhsA0HWtjU/mywDAEVafgV6RwQYTzVJ8J/f68n25aO0ebCdlsPU3s7Ddg00GGwDQbe0MNvNlAKCtX6s2wODtjWXkQNIOZJyUKdTOlDIluAizlIjsc2+8nRnGJQBAVzSvL/u28QkAWJzZAdAr+63Mj34tTMM8dmbodSWD7eLNUiKylcHWs40Cu60AowAbANBtMtgAgNMIsAG9stfK/HCKY7japQhPyGBr9WDzfbkIM2Ww9TizsNWD7YTALwBAV+zrwQYAnKJfqzbA4Mlgg4lZAjl7vi8XbpbAZ58zC9s92GSwAQDdtnfQ341PAMDizA6AXtmTkQNJ2j0iTuzB1uNeX101SwbXfo8XcnZmGJcAAF3RLLXet41PAMDi+rVqAwzaeDxuXQAJGDBkM/Vg07T9wjVLC10aYGZhO4NNiUgAoNv29GADAE5hNQ3ojWYQYXu0la3RaI1HA+s1dw82CwYXovW5jGfILOxZr4/m+5HBBgB0XbsFgSU0AKDN7ADojVYDahc/DNxuK5BxUqaUpu0XbXuGz2W/x5mFu60Aoww2AKDb9nq88QkAWFy/Vm2AQWv3X3Pxw7C1S0TOksFmSnARdmfILNzrcW+85vuRwQYAdF2fNz4BAIszOwB6o33x069FaZjXTIEc35kL1wxkXhofZDweX/ac/cH0YBNgAwC6TQ82AOA0AmxAb+zJxoFb7Ixu/Q6cWIqwlSnlO3MRtkZb2cqkP+Q4ycExAbZ2Nm6/Ppfm+xFgAwC6rt2GQIANAGjr16oNMGgufuBW7RKRJwXY+psp1WXbjWDm3jHlO5ufV98+l+b72dODDQDoOJs4AYDTmB0AveHiB261O0OvK30L16NdvvPyIFOfz2U7M5QuBQDoitYmTvNlAOCIfq3aAIMmgw1uNUsgw3dmPZrBzEvHZHH1+XNpB35lsAEA3aYHGwBwGgE2oDdk48Ctmt+B47KkknZ5wr5lSnVZ8//1cdmFfV7IaZUulcEGAHTc/rhZUt18GQBoMzsAesPFD9yqlcF2TJ+vpN+ZUl22Mzq9RGSrN96oX+ey1nsfH2Q8Hq/xaAAATtfMuO/bxicAYHH9WrUBBs3FD9xqllJ87axPU4KL0sriOib4uTfu77lsNBpdFmQDAOiig/G4Vc7bfBkAOMrsAOiNVtZHzxalYV6z9WDznVmH9mdzRgZbDz8XZSIBgE2wf2Qz2mg0WuPRAABdJMAG9IZsHLjV7gw92PZ7nCnVZWf1x2v3YOvfuWz3jB50AABdYK4MAJylf6s2wGA1L4D6mPUB85ilB9vegb6F63DWZ9P33njtDDYlIgGAbtrreVUBAGBxVtOA3uh71gfMo92D7aQSkXblrkOrB9lZGWyj/n0uuyMZbABA97Xnyq4vAYDLmSEAvSFYALeaJUvIrtz1OKsHWTsbt39Ttdb7H8tgAwC6yVwZADhL/1ZtgMFqXQD1MOsD5rHdzJIaH2Q8Hl/2nGZ5Qn0LL067RORZPdj6dy7bmSG7EgBg3cyVAYCzmCEAvdG6AOrhojTMY2s0uizIdtRez3t9dVWz7ONx2YX7Pd8tvXtGBh8AQBfIYAMAziLABvRG+wLI6Q2a34OjgYzxeNy6T1+Ji9POYLs8wNQMfPZxt/ROqwebEpEAQDdpQQAAnKV/qzbAYLkAgrZmptTRQMal8UEOi0ZujUbZ6mEgp6vOCjA1g2593C3dymA7JsAIANAFNnACAGcxQwB6Y79V7s7pDU7LlGp9X/QsvFDNDQCXzuzB1r9zWfM9yWADALqqtYHTfBkAOEb/Vm2AwWovSrsAgp2tk3t9+b6sz84ppTsPxgc5GE9yC0dJq49eX+jBBgBsAj3YAICz9G/VBhisVlk1OwyhtRCwdySQsT+W8bkup5WIPFrqdjQaXdhxXZSdU8YlAEBXNK8v+1hVAABYnBkC0Bt9L6sG82oGcmSwdcfOKT3I9gZQ6nb3lHEJANAV7XmZ+TIAcLl+rtwAg7TvAghaTu/B1sz4NB24SM3P5dLRDLbmTumeZuK2MtjGMtgAgG5qzpd3zJcBgGOYIQC9ISMH2nZP7cHWLkXIxWmWsD1aInIIvT70YAMANsEQ5mUAwGIE2IDeaPWUssMQTu11pafE+mzPmFnY18+l+b6OBhgBALqieX1pQxoAcJx+rtwAg7Qvgw1a2r2ujvb6siN3XU7vjdf/Urcy2ACATdCeL1s+AwAuZ4YA9EZ7YdrpDdq9ro70+lIicm1OCzANoddH833pwQYAdJX5MgBwln6u3ACDJIMN2pql+GSwdUfrczkS+NwbQCmi03oDAgB0hfkyAHAWATagN1wAQdtpgYwhZEp11fYpJSL3B3Ae21EiEgDYAObLAMBZzBCA3tgfuwCCplYpvqMZbGMlVdelFfgcn5xZuNPTz6X5vo6OSwCArhhCZQEAYDH9XLkBBqndg80FELQyhS7rwaak6rrsnJrB1jiPjfr5uTTflxKRAEBXtSsLWD4DAC5nhgD0wng8PhIwcHqDWXuw7fQ0kNNVpwU+98b9P4+1MtjGMtgAgG4yXwYAztLPlRtgcA7G44ynf94ajbKlRCScmim0L+NzbU4LfA6hB9tpvQEBALrCfBkAOIsVaKAXmrsL+1pWDebVzJQ6mik0hF5fXXVaichmqdu+lu5sjUs92ACAjjJfBgDOYoYA9ML+WD8pOOrUTKmxHbnr0i4ReXIGW18XcnZPGZcAAF1hvgwAnKWfKzfA4Oy1ync4tUFyeolIO3LXpx34PDmDra/ZuM0eJntKRAIAHWW+DACcxQwB6IV21kc/F6VhXs2FgL3x0R5syqquy86pvfH6fy7bPSWDDwCgK4bQGxcAWIwAG9AL7R5sTm2QHAlkHBztwdb/Xl9d1cpgO9obb9xcyOnnuawV+FUiEgDoqGaJyB0b0gCAY/Rz5QYYnNbFj2ABJDk9kLE/gEBOV+2MTi4RObgMNiUiAYCO2pPBBgCcwYoa0Avq48PlTitFuDeAQE5X7bRKJB5kPB7f+vfB9WCTwQYAdNN+q+KDa0wA4HJmCEAvtBalBQsgyem9rnxn1mdrNMrWaHTL3y81MnCHsFlgt1UiUwYbANBNMtgAgLP0c+UGGBzZOHC5donIUzLY9C28cCeViWwGnPq6kNM8R+8dXGpl8AEAdMW++TIAcAYzBKAX9u0uhMs0A2z7R3uwyWBbq50TsguHsFlgazTKdmOR6pIsNgCgY8bjsT7fAMCZBNiAXpCNA5c7tQfbuP+lCLuseZ5qZhe2Nwv093M5LbsSAGDdmsG17dFWq7w3AMCh/q7cAIMyhLJqMK/Te7DJ+lyn5v/zdg+2xk7pUX8/l91W8PfSKc8EALh4Q9n0BAAsxiwB6IV2WTWnNkiOZgm1gxhDCeR01fYJ5TubgdA+L+a0xuZYgA0A6BZzZQBgFv1duQEGRT8puFwrg+1IGT4ZbOt1UonI1mJOjz+X08YmAMC6mSsDALMQYAN6od2DzQUQJO0gztESkbI+12vnhPKdQ1nMab7/o9mVAADrpl8xADALswSgF4ZSVg3msdsKYhzJYNO3cK2a56lLjc9mfyCBz91WiUwZbABAt6iQAgDMor8rN8CgDKWsGsxj50ifr/F4fMvfZbCtVzPT9qQSkbs9zsZtv38ZbABAt5grAwCzMEsAemEoZdVgHlujrWxllCQZJzmYBtjG47FduWu2vXV8+c79Vjmi/n4uuyeUyAQA6ALXlwDALATYgF5o92BzaoNDzR23h70kDsbjjDMJto0yyrbvzIXbHR1fInHvYBjlblvjUgYbANAxrQopPa4qAAAspr8rN8Cg6CcFx2tlCk0XCvb0LFy7nVYGV7MH2zDK3R43LgEAukKPbwBgFmYJQC+okQ/HawZpDr8n+wfDKEPYZUf74yWTzMJmsK3P2bjN9yaDDQDoGj2+AYBZ9HflBhgU/aTgeDvHlCLc831Zu+M+l0tHgmuj0ejCj+ui7J6QwQcA0AV6sAEAsxBgA3phT0YOHKtdivCYDLYeZ0l12XGfy5DOY8dlVgIAdIUe3wDALMwSgF5o7zB0aoNDu1sy2LrouAy2Ie2U3j2mRCYAQFeokAIAzMIqNNALrRr5IxdAcOjYHmxjPQvXbeeYEontXh/9/lza41KJSACgW/bMlwGAGews64VKKR+f5OuSPCzJbpKXJ3lGrfX6OV7jiUm+IsmjklyV5G+SPD/Jt9Ra37CsYwX6pxkwsMMQbtXOlLq8FKHvy3oc97kMKoOtsRFCBhsA0DX7AyrdDQCc31K24ZRSrkvygiSPTvI7SV6S5COTPL+U8tQZX+PfJHleko9PUqd/TpIvSfJ7pZQHLuNYgX5q9y6ywxAO7R6TKbUv43Ptdo4r3TkeTq+P5vtvvm8AgC5olVQ3XwYATrDw6k0p5R5JfijJO5I8stb6pFrrJ2QSYLshyfeWUu55xms8OMm3JrkxyWNqrR9Ra31ykvsneWaS90nyY4seK9BfauTD8VqBjGMz2PodyOmqVonE8WEG23DOY63ArxKRAEDHyGADAGaxjFW1pye5Msl311r/6PDOWuvLkjwjyW2SnJXF9pTpsfynWutLGq+xl0nJyL9L8uGllPdfwvECPdTqKWWHIdyiXYrvMIPNgsG6NTPULh1msA3oc2ln8MlgAwC6ZW9sQxoAcLZlzBKeML19zjGPHd73xDNe4+Ykf5Dk/xx9YBpke+30r/eY//CAIWiV8HABBLc4rhTf3ng4mVJd1QowHVO6s+/nsea425PBBgB0TKukuvkyAHCCnUV+uJQySvLgJAdJXn3MU/5k+thDSimjWuv4uNeptX5Dkm844d+4/fTfSJI3LHK8QH/JyIHj7RxTim9fz8K1a38ul5fu7HsmbvP97evBBgB0zL6S6gDADBadJdwxk/KQb6213nz0wVrrfpK3JLldkqvP+W98TZKrkrys1vr68x4o0G/tnlL9XpiGeeweG8iRwbZuzRKRh5/H/ng457F2BpsAGwDQLUMq3Q0AnN9CGWxJbj+9ffcpz3nP9PaqJDfM8+KllCcl+bpMsuC+esafuS7JdTP+E9fOczxAd7VLeNhhCIfagZzJQkErg23k+7IOzYWaS+PjerD1+3Np92BTIhIA6JZW6e6eVxYAAM5v0QDb4UrQsaUfj5hrpaiU8olJfj7JdpJ/U2t90Yw/ep8kj53n3wI226XxQQ6mp6GtjLItYAC3aJUiPAzkDChTqqt2WwGmw8DncDILZbABAF3WnC/3feMTAHB+iwbY3jW9ve0pzzl87MZZX7SU8k+T/HAmx/fva63PmOOYXpfkN2Z87rVJ7jDHawMdJHsNTrZzRiBHyZv12D6mROTegDILm+9PBhsA0DVD2vgEAJzfogG2GzIJst2llLIz7bl2i1LKTpK7JLmp1vr2WV6wlPItSb4+k6y4f1lr/Z55DqjW+qwkz5rx33pRZLvBxtN/DU62M2r2YLs8kKNp+3ocl1l4eJv0/1zWymAby2ADALpFDzYAYBYLrarVWsdJ/jiTMo4POOYpZfpv/OFZr1VKGZVSfiST4Np7k3z2vME1YJj2XfzAiZoBtL3jMtj0lFiL3UYG16Xp5zKkhZxWgFGJSACgY5rzk92eVxYAAM5vGbOE66e3Tz7mscP7njfD63xXki/MJCvuE2qt/2PhIwMGYa9VvsPFDzQd24NN1ufaHZvBNqDMwnbgV4lIAKBb9pRUBwBmsIzVmx9LclOSrymlPOLwzlLKI5N8dZL3JHlm4/77lVIeWEq5Q+O+JyT5l0n2k3xSrXXWHmoA2W82oJaNAy27x/Vg07R97Y7vwTachRwZbABAlzXny33f+AQAnN+iPdhSa31dKeUrk/xAkpeUUn59+tDHTl//H9da39z4kRcmef8k/yS39kr7punt3yb5klLKl5zwz31rrfXVix4z0C+yceBkzaDzYQBn33dm7Y4NfA7oc2n1YBNgAwA6ZkiluwGA81s4wJYktdZnllL+KpOMtcdk0kPtxZkExF542s+WUu6U5FHTv94zyeed8vQfSSLABrS0+knZXQgtu8dkCrUypfSUWIuzSnf2/XNpvr/D9w8A0BX7rTYEAmwAwPGWEmBLklrrc5M8d4bn3efI39+WZLSs4wCGRwYbnKwZdN4bDy9TqqtaAabDzMLxcBZyZLABAF3W3vjU73kZAHB+/d4eDQxCuweb0xo07WxdHsjZGw+n11dXtT6X8WFm4XB64x0dl+PxeI1HAwDQ1t741O95GQBwfmYJwMbbU74DTtTccbuvB1tntEpE3vK5DOdctjXaytZoUsBgnHEOBNgAgI4Yj8eD2vgEAJyfWQKw8fY1oIYTtUrxDTBTqqu2G9m2l8YHOTi6kDOAUkS7o8vHJgDAul1qZK9tjUbZUiUFADiBWQKw8do92JzWoKldiu+wB9twMqW6ams0uizI1ix3O4TPpZ3FJ8AGAHSDCikAwKysRAMbrxkskMEGbWeViNS3cH2a/+/3Dw5aizlDyCxsbohovncAgHXaH1hVAQDg/Pq/egP03p5gAZyoGcQ4zJBqluMTlF6fo9mF+wMr3XlcdiUAwLrtjVVIAQBmY6YAbLz9sRIecJJmAG3vlgw235kuaJVIHB8cKXfb/8+l1YNNBhsA0BEqpAAAsxJgAzZeK4PNBRC07B6TJSTrsxuOlohsbhYYQjmidoBRBhsA0A2tTU/mygDAKcwUgI23f6CEB5zk+B5sMti64GiAaWgZbDutHmwCbABAN8hgAwBmZSUa2HjN0mJDyPqAebSCGONjMtgEpdfmsgy21mJO/z+XZhBxX4lIAKAjzJUBgFmZKQAbb388rKwPmMf2aCuj6Z8PxuPsH1zKQcZJktH0cdZj50j5zuFlsDV7sMlgAwC6wfUlADArq2rAxrPDEE42Go1agYybLu3d8uedre2MRqPjfowL0C4RedAqdzuE3ni7o8v7AwIArJse3wDArPq/egP0nn5ScLpmsOY9+7cG2PQsXK/m57J3cCn742H1+2hlsI2ViAQAusH1JQAwKytrwMbbG1jWB8yrGch4TzODTc/CtWpm3N50af+WP2+PtrI1gMzC3S0ZbABA97i+BABmZaYAbLxm1ocdhnC5ZiCjncHm+7JOJ5XuHEpmYbsHmww2AKAbXF8CALMaxgoO0Gtq5MPpTsxgG0ggp6uaO6Jv2h9eZmHzfcpgAwC6Yl+PbwBgRmYKwMZrXgDZYQiXa/dgu/mWP/u+rFcrs/DS8DILd1s92ATYAIBu2HN9CQDMSIAN2Hh7dhjCqVoZbK1MKd+XddoeHV8icijnsR092ACADtpvlK4eSmUBAOB8hrGCA/Ra8wLIDkO43O6JJSJ9X9ZpZ+C98Xb1YAMAOqidwWbZDAA4mZkCsPFaNfJl5MBlWr2+BliKsKuaATYZbDLYAIBu2B83MtjMlwGAUwxjBQfotb2xDDY4ze4JmVJDCeR01e7o+NKdQzmPNd+/DDYAoCv0YAMAZmVlDdh4rQw2F0BwmXYPtptv+fOunhJrtX1SBttAPpfmuNwfy2ADALpBhRQAYFZmCsDG22v1YHNag6P0YOumnRMz2IZxHttVIhIA6KA9Pb4BgBkNYwUH6LX2DkMXQHDUyT3YTAPWaffEHmzDOI8136cSkQBAVzQz65VUBwBOY6YAbLy9sRr5cJp2icjhBXK6qrlg855LA+zB1iwRKYMNAOgIPdgAgFkJsAEb7WB8kIPxOEkySrI1Gq33gKCDWoGcVoDNNGCdmhm3NzU/l4H0+mi+zz0BNgCgI/YbmfU2pAEApxnGCg7QW0cvfkYCbHCZdg+2m4+9n4vXDHAeZHzLn4fyubQy2MZKRAIA3dDOYLNsBgCczEwB2GjtBtROaXCcdg+2/cb9wwjkdNVJmWpDySxsvk8ZbABAV7Q2cZovAwCnGMYKDtBbrQbULn7gWCeVthlKplRXnfS5DKUUUbsHmww2AKAb9PgGAGYlwAZsNA2o4WwnZUQNJVOqq076/787kM0CzU0R+zLYAICOaM5LzJcBgNOYKQAbrX3xM4xFaZjXSQEbQen1Oinrdijnsub4a+4UBwBYp3YbgmHMywCA8xFgAzaaHmxwthNLEZ7QA4yLcWIG20DOZc33r0QkANAVrU2cA6ksAACczzBWcIDeksEGZzspYGNH7nqdXLpzGJ9LK4NNiUgAoCPabQgsmwEAJzNTADba3vjWrAfZOHC8k74bQwnkdNVJO6KHspAjgw0A6KL95jWm+TIAcIphrOAAvbXf2l3o4geOc9J3YyiBnK6SwSaDDQDonj3XmADAjKysARttr1Ui0ikNjnNyDzYLBut0YgbbQD6X7dFWRhklSQ4yzqWxLDYAYL3G43Ers941JgBwGjMFYKM1L37sLoTjnbQw4DuzXidnsA1nerarTCQA0CEH43HGGSdJtjLKtjYEAMApzBSAjbY3bmSwDSTrA+Z10ndjSIGcLjqpROeQAp/N7Mp9ZSIBgDVrXV+aKwMAZzBbADaaHmxwtpN7sPnOrNP2iYHP4Xwu+rABAF3i+hIAmIcAG7DR9tTHhzMpRdhNJ5buHFApop3Ge93Xgw0AWLP29aUAGwBwuuGs4AC9ZIchnE0Ptm7aOSGQNqTFHBlsAECXtK8vLZkBAKczWwA2mgw2ONvuSaUI9S1cq9FodGyQbUiLOc3z9v6BDDYAYL1a15fmygDAGYazggP0UmuHoQsgONZJGVEy2NbvuI0BMtgAANZjf6xCCgAwOwE2YKPtNS6AhrQoDfM4KSNK1uf6bR+zMWBIiznNneH7AmwAwJo1N/yYKwMAZzFbADaaGvlwtpOCzyf1AOPiHHfeGlI5oub7b26YAABYh2bJ6iFtegIAzsfKGrDR9ls92FwAwXG2jwmk7Yy2MhqN1nA0NB23M3pImwWa52092ACAdWtlsNmMBgCcwWwB2Gh7B2rkw1m2RqPLFgh8X7rhuGy1IZUj0oMNAOiSfdeXAMAchrOCA/TS/riRwWaHIZzoaNBGxmc3HP1ctkajbA3oXNZ8/zLYAIB12xurkAIAzG44KzhAL8lgg9kczZQaUhnCLht6ZqEMNgCgS/T4BgDmYbYAbLTmBdCQyqrBvGSwddPRz+G4kpF91gww7o8F2ACA9drT4xsAmIPVaGCjNS+Ahpb5AfM4+v3YHVggp6uOBj6HtlO6ncGmRCQAsF6tDZzmywDAGYa1igP0TjPjwQUQnOyyTKmBBXK66miJyKHtlG6+330lIgGANdsbKxEJAMzObAHYaHqwwWyG3uurq44G1Ib2uTQXrvRgAwDWrd2CYFjzMgBgfgJswEbbb9XId0qDk1zeg833pQsuy2AbDetzaWYe74+ViAQA1qvdgmBY8zIAYH5mC8BGk8EGs7msB5vvSyfIYGv2YJPBBgCslww2AGAeAmzARpPBBrO5rAebnoWdMPTMwub7bZ7PAQDWoZXBZr4MAJxhWKs4QO+0MthcAMGJdi/rwWYK0AVD740ngw0A6JL9sQw2AGB2VteAjeYCCGZzWQab70snDP1zaQYY9wXYAIA122+1ILBkBgCczmwB2Gh7SkTCTI4uEBzNnGI9LisRObDPpRlQ3BsrEQkArFfr+nJg8zIAYH5mC8DGOhiPc2nsAghmcTQzamilCLvqaC+8oX0uzfcrgw0AWLd2Btuw5mUAwPysRgMbq3nxszPaymg0WuPRQLcdDUAPrRRhV12WwTawTNzm+9WDDQBYt+Z8xHwZADjLsFZxgF7Zb2Sv2V0Ip7s8g80UoAuOBj6Hdi5rZ7ApEQkArJdrTABgHlbXgI1ldyHM7ugCge9MNxz9HI6WjOy75vuVwQYArFv7GtOSGQBwOrMFYGM1sx1k48Dphp4p1VVHz11DO5c1329zxzgAwDq0rzHNlwGA0w1rFQfoFRlsMLvLM6VMAbpge+C98ZrvVwYbALBue0f6fAMAnMZsAdhY++NbL352B1ZWDeZ1tMSNHbndcHlvvGF9Lu0ebAJsAMB6ta4xBzYvAwDmJ8AGbCz18WF2l/dg853pgt2jGWwD2yndfL97B0pEAgDr1ZyPDK2yAAAwv2Gt4gC9oj4+zE4Ptm6SwdbIYBvLYAMA1quZUT+03rgAwPzMFoCNJYMNZndZBpuyqp1weQ+2YZ3Lmu9XDzYAYN2amzjNlwGAswxrFQfoFRlsMDs92Lrp6M7ooX0u26OtjKZ/PhiPczAer/V4AIBh2zvQgw0AmJ0AG7CxmuXE7C6E0+nB1k1HS0QOrdfHaDRqved9WWwAwJpcGh/kIJPNPqMkW6PR6T8AAAye1TVgY+2pjw8zOxqEtiO3Gy7PLBzeuWy3VSby4JRnAgCsTqs85NZ2RgJsAMAZdpb1QqWUj0/ydUkelmQ3ycuTPKPWev0cr/GAJP8+yWOS3DnJnyX5r0l+oNZqxQVoOXoBBJzsaCBHBls37BztwTbAbNzJe95L0s5MBgC4SPs2cAIAc1rKjKGUcl2SFyR5dJLfSfKSJB+Z5PmllKfO+BoPS/KyJJ+d5C+TXJ/k3kn+c5KfWMZxAv2iPj7M7rJMqQEGcrro6OaAIZ7Lmu95T4lIAGBNmvOQIW56AgDmt3CArZRyjyQ/lOQdSR5Za31SrfUTMgmw3ZDke0sp9zzjNUaZBNGuSfKUWutjaq2fluQBSf4gyeeVUj590WMF+mV/3MhgG9lhCKe5vAebRYMuuCyDbYC7pZvveV+JSABgTZrXl0Pc9AQAzG8ZqzhPT3Jlku+utf7R4Z211pcleUaS2yQ5K4vt8UkemuRFtdZnN17j75I8bfrXL1/CsQI9IoMNZnd5D7bhBXK6SAabDDYAoBtaGWwDnJMBAPNbxuraE6a3zznmscP7nnje16i1/laSNyd5TCnl6vkPD+ir/dYFkGABnObyHmwWDbrgss9lgOWImll8erABAOvSzKS3GQ0AmMVCM4ZpaccHJzlI8upjnvIn08ceMn3uSR4yvf2jEx6vmRzrg895qEAP7R0o4QGzOvod8Z3phqMlIoe4mLPTymBTIhIAWA8ZbADAvHYW/Pk7ZlIe8u9qrTcffbDWul9KeUuSuya5OpOebMd53+nt35zw+OH9d1vgWFnAu/Zuzs/8+cvylze+bd2HAre4ce+mW/7sAghOdzSQs61vYScc/RyGeC5rBnt/9DX/N1dsLzo9BQCY396lRoDNXBkAmMGiKxi3n96++5TnvGd6e1VODrCd9TrN1zhVKeW6JNed9bypa2d83uC9/C1/lZf93V+u+zDgRFduWZCF01yxvZNRRhlnnCu2trM1Oi2xnIsyGo1y5fZO3ntpP6MkVwwwwHZlI6D29pvfc8ozAQAuxm1s+AEAZrDojOFwe894hueetv1n1teZZQvRfZI8dobnMYcH3OF9ctXOlblx/73rPhS4zD+44rZ52J3vte7DgE7b3drOR979A/LiN/15Hvu+D1j34dDw2Pf9wLzgDa/Oo+92v0Fmbz36bh+QP/77v2mVZQIAWJed0VYefbf7rfswAIANsOgqzrumt7c95TmHj924wOvM8hqHXpfkN2Z4XjLJYLvDjM8dtLvf7g75Dx/25Lztpned/WS4YHe57VXK3cEMnvKBH5bPuO/Dc9udK9Z9KDR8+n0fnifd+4Ny253ddR/KWjzszvfKd3zYp+aGm286+8kAACt29RW3ye3MlwGAGSwaYLshk+DYXUopO7XW/eaDpZSdJHdJclOt9e2nvM4bMwl23T3Ja455/KwebbeotT4rybPOet70+F4U2W4z293azt1ud826DwOABQiuddNQg2uHbrtzhbEJAAAAbJSFUj5qreMkf5xkO8lx9abK9N/4wzNe6o+mtw++7AVKGSV5YCZlJF917oMFAAAAAACAJVhGTbXrp7dPPuaxw/uet8BrPDrJ+yR5ca31nXMeGwAAAAAAACzVMgJsP5bkpiRfU0p5xOGdpZRHJvnqJO9J8szG/fcrpTywlNLsffYbmWTCPb6U8sWN575P42e/awnHCgAAAAAAAAtZOMBWa31dkq9Mck2Sl5RSri+lXJ/kt5JcneSptdY3N37khUleneRTG69xkOSfJrkxyX8ppfx2KeV/JqlJHprkv9Zaf3nRYwUAAAAAAIBFLSODLbXWZyb55CS/neQxSR6V5MVJHl9rffaMr/HSJB+W5BeSfGCSf5jkL5N8aZKnLeM4AQAAAAAAYFE7y3qhWutzkzx3hufd55THXpXkM5Z1TAAAAAAAALBsS8lgAwAAAAAAgKEQYAMAAAAAAIA5CLABAAAAAADAHATYAAAAAAAAYA4CbAAAAAAAADAHATYAAAAAAACYgwAbAAAAAAAAzEGADQAAAAAAAOYgwAYAAAAAAABzEGADAAAAAACAOQiwAQAAAAAAwBwE2AAAAAAAAGAOO+s+gDW7f5K8+tWvzlOe8pR1HwsAAAAAAAAX5NWvfvXhH+8/788OPcB2VZK8853vzEtf+tJ1HwsAAAAAAAAX76p5f2DoAbbXJrlvkhuT/Nmaj2Udrk1yhyTvSPKKtR4JfXZtjDNW69oYY6zWtTHGWK1rY4yxetfGOGP1ro1xxmpdG2OM1bo2xhird22MM1br2hhj87h/JsG11877g4MOsNVaH77uY1inUsqLkjw2yStqrR+z3qOhr4wzVs0YY9WMMVbNGOMiGGdcBOOMVTPGWDVjjItgnLFqxtjF2Vr3AQAAAAAAAMAmEWADAAAAAACAOQiwAQAAAAAAwBwE2AAAAAAAAGAOAmwAAAAAAAAwBwE2AAAAAAAAmIMAGwAAAAAAAMxBgA0AAAAAAADmIMAGAAAAAAAAc9hZ9wGwVs9K8qIkr1vrUdB3z4pxxmo9K8YYq/WsGGOs1rNijLF6z4pxxuo9K8YZq/WsGGOs1rNijLF6z4pxxmo9K8bYhRiNx+N1HwMAAAAAAABsDCUiAQAAAAAAYA4CbAAAAAAAADAHATYAAAAAAACYgwAbAAAAAAAAzEGADQAAAAAAzqmUMlr3MQAXT4ANAAAAgLWxMA30wHbifAZDI8AGzKWU4rzBShljADAbCzisUilld3prnLESpZRPKKX80+lfjTNgI5VSPqqU8ptJ/lWS1FrHaz4k4ALtrPsAWEwp5S5JSpI3J/mrWut7p/dv1VoP1npw9EYp5Zokt0vyzlrruxr3j0wcWIZSyp2S3DvJG2utf9e43xhjaUop/y7Jf6+11nUfC/1USrlrkg9L8vYkb0ry2lrr/loPit5p/M78u1rrGxv3+53JUkzH2Hcn+e0kP2hcsWyllHsn+Z4kn5rkr0opP1trffd6j4q+KaXcPcmTkrw7yZ8l+YNa683rPSr6ZHou+89J/tH0rhdP77cmy9KUUt4nybVJ3pLk9bXWt0zvN846QpbABiulfHOSP0zyvCQ1yfWllC9a71HRN9MF6Rcn+Y0kry6l/FAp5QmJXTksRynlm5L8QZIXJHl9KeUnSymfvObDomdKKY9J8o1Jvm7Nh0JPTedlr07yvzL5nfn7SX5gGnSDpSil/Pskv5fkV5K8oZTyS6WUz1nzYdE/n5fkKUk+oZTywYksNpajlDIqpXx7kr9M8olJfizJlwiusWyllG9I8tokP5Lkp5O8NMmPTBeqYSFHzmWPy2RNNplssIugB8syvcZ8VZLnJnl5kheXUr4+Mc66RIBtA5VSHlhKeXGSr88kc+2XMgmAPDbJfymlfHCt9UCZNRZRSnlYKeWlmSxIj5O8MpNJwz9N8rxSyueXUm4/fa4LbuZWSrlnKeUXk/zbJH+X5P/LZOLweUn+VynlUzLNtDbGOK/G78J/ML19SinlY9d0OPRQKeUBpZRfz2Re9tok35fku5LckOSLk3xzKeVeazxEemA6//+/Sf7fJO9I8sIkv5Xkk5L8t1LKB9Vax6WU7XUeJ5utMd+6zfT2o5N8Uill18Y6FlVK+dwkf5PkazKZ9/+TJF9ea/2VtR4YvVJKuaqU8v1JviHJ7yb5piRfnuSvkvw/ST50jYdHD5RSnpJJIO3wXPYlSX58+vArps+xfsFCSin3K6X8WibXmH+ZyYaU/5HkAzK5vvzo6fPM/TtAAGYzfXaSRyf5r0k+udb6lCQfm+QZ08e/MBHJZmFfmOSRmUwU/lGt9TNrrY9P8o8zmUx8R5LPTGSycW4fm+SJSX4ikzH2WUkekeQrktycySL1pyXGGAs5vLh5YuO+bzzsKwNL8DlJPibJs5J8eq31X9Ra/3Um2R+/l+TTk3z42o6OvvicJB+Ryfz/U2qtn5fJuHtGkiuT/MskqbVeWtcBsvka860HJnlvJoG2z8xk7MG5lVKelOTZSa7KpEfRU2qtP3PYfsDmYJbo2iSfn+T/JPnHtdZvrLV+f5JPTvKIWuv/XufBsblKKQ8tpfxBJmtkb0nyr5N8Qa31vye50/RpVyXWL1iKT84kO/LZST6t1vqltdbPTvK108e/LDH37wqTmA1TSrlDki9N8hdJvqbW+lellO1pf4/nTZ/29lLKlh0TnNe0FMyXJHlNkq+otb6usSvi5zKpMX2nJE8tpTx2+jPGG/P6oiSXknxr41w2TvIDSb45yZ2T/ItSyocnxhjnU2u9NO0l8zFJ3pbkdUkek0nwAxYy7YX75Un+NJPfl3/Z+H3565mUirxTkgdMn+88xtxKKbdJ8tQkf57J/P8vSyk70/n/z0+fNiql7FqkZhGN89eVmWRK/kyShyX5tFLKHafPcR5jbrXW52WyA/9tSV5wpH/k7ZL8g1LK7Q/Hl3MZC/icJNck+e5a62sb97+61vqq5tgyzpjVtHrTlyS5W5LvTfK5tdbvqrW+YfqUO2aySfj10+f7Xcm5lVJ2kvyzTOZi/266Xna4QfgXp7fvKKVcYax1g18mm+cemUwWbqi1vn1632Gm2scleUOS/11rPTjcMeHLxqwaY+V+SXaT/EWt9R2llNF0kXo03R3x/CQ3ZnLB/fmllKvs0GFW03rlVyXZTvL3mZRUy5Ex9pOZLOp8eJLrSim3N8Y4TSnlkdNG5sf93nt6kgdlssvwy6b3/b96Y7EE98rkgvqNtdYbGr8vt6bnrN+dPu8Rid2snNu9ktw1yTtrrW+fjq/96WMlk3Kkr6y17qlgwSKm56/tJPfOpMzVzyX5s0wqCnzM9DnOY8ylEcT4qSR3T/LwxmP/KsnvZJJt9H+TfEcp5X2cy5hXY5xtZ7JG9srGY4/OZO7/E0l+vpTy1FLKFcYZs5jO79+VyUbgf5TkX9VaXzl97HBjym6SK5LcN/G7koUdrlO8I5N1/tRa96b3fdL0vp+rtd5srHWDAFtHlVLev5RydSnlttO/byVJrfXVmXyRri2lfEEp5c7Tfgufk8nO1nsl+dFSyg+XUj5r+jO+bBzr6DjLraXU3p7kpiRbpZS7NsbQ4eOvTfKeTMrGPDHJ4y/okNkwxwU9aq3jWuuNmUxA75Hkow4fPxxrtda/yqTc2qszWdR50sUfPZuilPIZmTQu/9Sk/XuvlHLnJE/OJHPtRdOyMM9L8v6ZZB7BmU4J4L4+kx6S73fYAytplem+Znr7J9OfNffmRKeMs79N8u4kby6l3OVwfJVSHpHk6zIZZ59WSvnuUsoXTjPe4DJnbEbJNHh7Kcltk7y31np9kp/NZL722aWU+17oAbNxTpj7H/5O/O1MMjweU0p532n/0u/MpJ9Mktw/k/KRv1BK+UcXe+RskjPG2R0zWes8rB7wSUl+JJMepv9PJtcFP5RJoO3jL/bI2RTNMZbp2nmt9VW11t+ersEezukPpmPw7dO/v3368+b8nOmkedk0y/utmaxZ/Msy6fm9O13n/5pM1v5/uJTyP0spT5tmgrNGvvAdU0p5cCnlOZmUFfqtJM8rpfzDJLdvPO3fT2//a5KXlFJekclusGuSvDyTkmtfnORnSinffLhDXyYbh04ZZ1dNn3KQ5M2Z1C//wMOfa0xaPy6THTrfmeR9k3ziYdkYOHRS0KMx2Tysf//RjWyPpldkksl25ySfMi3FBsc5PE/9w1LKw5LWxfZbk7wqyTc3ysR8cya/K7+qlPJBF32wbJbTArhTP5/kxZn0Yjj8mcM51/2nt++Y/qyd0hzrjHH27kyyIJ9Ya33L9PkPTvLdSR6cSaD3nkn+RSbXBz9YSnm/6fPM/0ky07kstdaD6Tzt/ZL89fTuX87keuGJmVwDpExK+V09/bMxRpJT5/6HY+SPMymp/Dm5tbrAMzKpivKwJP8wyXMz6Tf/b0op9zvy83DaODvMJHrh9PaR09+F/yHJ1dPnPzqTzZu/m+QTM+nLfP/pzxtnJDl2jF3W4+pwTj/dPDzOrevrD20+DieZ4Vz2jdPbZ2Ryrfn7mVR5uu3073+ayYaBH8gk2Pb+0593LlsDAbYOKLfWGb8uyW9m0sTwxiT7SR6b5H9mMilIktRafyrJt2SyA+yemZzAfyWTi+5HZZIN8s8zyTJ6eiaNqWWyDdw846zW+n8yGVN3T/LVhzu7pjsmPieTwNqLMskwemkmJWPueVHvhY1xbNCjMdn8/Uz6MDwukxJXLdMyDL+aSaDtsUkeuOoDZrM0grUfPL39+CRPKqVcOd1ZeFin/KsyCdYmSWqtv5PkBzPJovz6izpeNtZx57LDnaxvzSR4+wW11jcd/sB0/I0yaU49zq2LPXCS0zYKXKq1/smRufxNmQTe/lUmwbePz2SD3SuTfEEmlS3M/2k6cYwdmv79faZ/PVxQ/P0kPz398z8upXxlku/P5HrTGKPppLn/YXb3azMpA3l1kq9N8t9qrV9ba/2zJONa628l+TeZBHU/LMnnTn/OGKPppHF2eM56eZI3JXlUJr8X753kybXW/5XkFbXW5yS5LpM+Ro+e/tk4o+nM35eHGtejLztyvyAHZzn1XFZr/ZVM+rD9YibXkw/OpL/3p9RaPzrJJyT5jEw2QX1ekqdNf865bA0E2DpgughzTZKvyKRkwhcleXit9eGZXCy/OcnTSilfXaal/Gqt/276hfrXmXzBvqDW+pvTx27MJAX+GZk0qH5CKeV9L/ht0TEzjrMvLaV87fRHvjeTINsnZ5Lh9qtJfi+ToNrtkjwzyZ9nkhnyAZnu0pcKzwxBj8MdOa/IZKPAo5M8rpRy5TEv96pMejLcK9MgXOPnGbjpTvu7ZbLr6+3Tuz8nk0WZW+qU11rfVGvdL5P+f4fj5z8keWOSzyqlKEHKZc44lx1meeQwsHbMuelDMilP9LIkv+tCm+Oc9TvzpJ+rtf5FJvP/76m1vqXW+rokP5bJpoEbkjyxlPKBJ/08wzHDvKxZkmicSf+ia5L81fS+/UyqDrwskznbt2YSxN0zJyOZbYw1nvNDjR/9ienPb2eyeJhMysP/WCYbCK5VJYVDM4yzw8ffkEl2xydlEmD73SR/dOTlXp3JmtmNST68lHKPlR48G2Ge35eHGpuHD38fPmzFh8mGm+d3Zq31h2qtn57JOu5LkvyTWuuLp48dJHlOJtV53pFJZTGb0tfEQnh3fFQmmWjfU2v9uenCzXat9dcz2YF6QyYXzI8/vJCZLir+iyQHtdY3Ny9wpouKv5XJ4uFDpz8Ps4yzf1NK+Ue11lcl+aeZZKu9PskjM9n99StJPqbW+hu11vdmcpI/fG2p8BwGPe6ek4Mehzty/iqTCcF7MlmoeUjzdaa7d27KJMCWTEoTHVuigf47Lng//b33zzPZTPKdSX40yQcl+fRSyp2mz2ktHNZaL01Lkr4xyXdMH/qGcmsvSkgy07ns4Mjzj56bPnp6+5LpovWuIBtHnTXOzvjZvz36Wkn+IJMybO+XycIhAzfDuexoIPdumfRZfmuSlFI+IMlTMpmnbWWyUe+La63fZU5GMtsYa/zOfF0m1Xg+qdZap49famS5jafPeU8mu/UPX4+Bm3VeNv3d+L8zWcO4a5J311rfeySbcpxJebU/z2QTp9+XnOf3ZfNa89ent48opbyPLCJOMs/vzFLK9jRR4ulJrqm1vq6UstN8rUzm/r+fSVWx917YG6FFgG1NjlkofOj09rCvwm5jEfrXMlkEvF0mAbXDUmq7mTScflMp5faHz2+c4P88kwugu2bSw4iBWWCcfUUp5cG11r+ptX51JrtVPzTJh9ZaP6XWWhul1w57Gh3u4HdeGZBTgh7/LKcHPQ4nBc/JpAzMo5J8/nTjwOHrHp7LXpbJLtZ3nfRv0k+llK1Syl2nAbHjgvd3zWTR762ZjLPnJqlJPj2T0qMnlUg4vO/7M9nV+qhMNhQwUAucy04qF3Nlks+a/vX6JKm13jzdlXjHUsoTSykPOe5n6a9ljbMTXufwOW/OJDhy57R7ODMASxpjh78j31VKeUqSZ2cSEHlvkudlMq4+aLo4pAzWwCwwxg534984rcbzvGNe53As/UUmAY/bJrmDMTY8S7jGfF4mG873kzyslPLBh5VUGuPpLzPpPX/3JHp9D8yy5mSNzLa3ZNKK5aokMm9Jsvg4m67XjjKpGnbjNHi733xOkr/LZO7/D2LuvzYWKS/QcQuFjQnAYRPpD5xmFO0d/sz0/v+WySThcUk+pZRym0y+OH+bSd+FW5oZNhYTH5DkPplEs2/pC0K/LWmcPTaTcXb7ZLILrNb6Z7XWP5k+f/fwZzMpgZVMsyRlsPXfkoIe+9Off0smZWBemUmA4/Onjx80XvvaTAK/h+X+jLEBKJN+kf83k6zZXy+l/NtSyh2OPO0umew+/XfT3aovTvLfM9l9/1mllPtMX+u4C6Ht6YT1m6d3f00pRS/JAVlFALcx1h6aycaU38vkYjtlUu7jCZmUJPqfmYzR3dBrqxhn052vo1LKvRr3HT7nvpmU7f693Drvo8dWMMYON3N+7fT5j0jy9bXW98+kP9YfZ9Lz42OO+Vl6aElj7LKfm77u+x3+vTGWHpJJ5ZTX1FrfbowNw5KvMf8uk/7Lr8yk1cCXTB+/1BhP98wk2/uPM1lXo+dWtXlzet9hVuT9orXFoK1gnF2Z5E8yWfc/XH/NkXPZ/ZO8Jub+ayPAdkFOWig8jDxnstv0hiQPTGO3QyPN/W8y2T341ky+iPfOJEPtZZkE0r6ylHLt4ReslPJhmfTI2k7yH2qtN6/8TbJ2Sx5nn5dG4LaU8rBSyleVUj68EZj7qEyyKv84yS9dwFtkzZYZ9MitGWr/XyY7d25K8u2llC+ebiJIKeVRSf5dkrcl+a+rel90x3Qy+rOZBPzfL5O5yocl+aYkP1dK+ZjG0/80yTck+dnpBPamTLIifyvJE6b/nXQhdJi9+8tJ/kcmF9/fuJI3ReesKoDbGGuH5f2ur7W+u5TyoUm+O5PfsZ+a5H8l+c7GZhV6aFXjbLq79ZuS/FAp5RMb9394JgHc2yb59lrre1b01uiIJY+xw4XAv5rePjTJTyd531rrt0/ve12Sn8lkMee6Usr7rOBt0SErPI/dI8l/SvID080nh/d/6PT+d2fSU54BWNE15q9l0vPvTUn+WSnl60sp953+ex+W5Aczyfb+z7XWd63qvdENq9y8OU10eM/0+UnyhYnWFkO05HF2GLN5a5LfziTb9qtKKY9r/Hsflsm6yV0yubb8+xW9Nc4gwLZiMywUfuz0qa/N5ILlCUkedOQ1Dk/ez81kkvCgJI+fBk0OMz/+SZLnlFL+cynl5zNZLPyIJD+eyeI1PbaicfaQTPuqZZI99IQk/3H6el9XSvnBTBZx3ieTYO6bjk406I8VBT0Oe2EdJPnZJF+eZCeT8fT7pZQXZZLl8cgkz0ryx8bYIHxCks9M8nNJnlRrfVgmZWq/J5MGwP/tcEGv1npTrfUl04nkYU+FV2QynpLkc0opD0+OL2HVWEz8jkzOj7+2ovdER1xEAHc61g6bVr+rlPIVmeyi/vLpaz6q1vrZtVb9cXvqAsbZTpI7JHlSkp8qpfxyKeW5SX4hyUdmcn3w66G3VjUvmz7/NUm+KpPS8NfVWt9WJmXVtmutN2ZS9vY/J/maaZYIPXQB57ErM1kQ/MQkP11K+YVSyi8l+fncuo7xu+b+/bbia8xLmcy/vnL689+c5A9LKa9K8vzp858dG4V77aI2b079TCbZkE86XINrBEnosRWNs4Nya9Wd/57JWu3HJfkfpZT/UUp5XiZz/8cl+Yk4l62VL/rqnbVQ+KOllLvWWmsmX5YrMmleeItpKaut6Y6In5vefbgj4lenf/7FTKLdT0/ySZnULP/cWutTRbAHYVXj7Iunj70ryfcm+YFMLoa+JZMyC1ck+Zxa6w/WSSNO5Tv6ayVBj8bjl2qt/yPJp2SyQeDOmdSZfnMmY+yr6qRngzHWI8fs/tvJ5Lzz1iT/vNb6yiSptf5+rfVfZfK77j5JvrOUctvmz9Zb698nk4vm6zMZo59SSrntkccPf+bSdMfhy2ut96u1/vcVvE26ZeUB3Ol56rB035dmshP/miRPqbV+RK315St9h3TBSsdZrfXNSb5++npvyWSB+jGZZHt/bq31S8z/e29V87LUWv++1vqfaq2/WyZljram87TDzO/fr7V+xeHvaHpr1eex1yb515lUqLgpk+zuj0vyzkzOY19Wa32nuX/vrfoa8+Za689kskb2w0len+Q9mWxS/+xa6z+uk5YF9NeFzP2ngZD3ZpIZuZNJKWXtLYZj1eey30vy1EwCePuZjK+PyOQ64HOnG6Kcy9ZoNB6bryxLafc/O1wo/PVMMoUedHSwl1J+IZOJ5E/XWj+/TMokvCzJ+yb5pHpM49/pz/3x9DkfVWv94+l9t8lk0fE2Sa6qtb74uJ9l861hnD2m1vqqxr91j0wCH7eptV6/9DfI2i0wxn4yyZfWIyWpDl9vmur+H6fP/ZYk/7HW+p7GhXbz37xdkvertb5mFe+R7pmOgzsn+bNMSrk8qtb6zumuv9E0GPYBmfSJfEAm5ZJ/5qSLllLKp2Wyw/6GJF9ea5WdNjDrOJdNH78ikwzvz5/+6LfXWr9+Ne+SdVvDONtKMp4+ZyvJnZJcleQutdbfXdkbZW3WeC5r/bv015rPYzuZlL26Kskda60vWdkbZa26cI05/bn3rZPWGPTMun5fHvmZByT5hFrr9y3/HdIF6/idebjmUSYVeO6aSZWxO5r7d4cA24qcY6Hwn9Raf7yU8mWZZAr9XpJPqbW+sfGaO3XStPV/Jnlskmtrra938TNcFznOLvit0RHrCHqUk5vB0hOllA/KpInvXpJLmZQAfct0PP1Bkt0kn9oMsDYmnl+Syc7A30/y6bXW1x157cPn3THJv88kW/eHknxDrfXv/M4cpos+l5VSvjeTygJfUWt906reF91iowCrZoyxasYYF2FN15iHpdYYAOcyLoJxxiElIpeglPJBpZRvKJO+VF9TSrlbkq1p1PqvMknpvGcySQ+ut5ak+otMGt4nyb8spdw9k0XDX0nyIUm+skyyjVJKubJOeq4lky/lXiZpx6fV/6VH1j3O6L8ljbF/lUnN6aOvfVhC4f/LpK/aA5N8WpmmyTceV0ahx0op15RS/kuSP8ik7vi3JPn2JC/MtCRtkr9M8oFJ7j39maO9N348kwnqw5M8+ehzDn8n1knJhZ/PJBD3WUk+ufk4/bXmc9nu9PGvqpM+a4JrPdWV35n0lzHGqhljXISujDPBtf7qyhij34wzTiPAtoAlLxQ+NMnnT4Mb357JguA/T/J1pZQ711rfW0rZKaV8eZIHJ/m5OqlbTs8ZZ6yaoAcXoZRy30xqkn9RJmPlizPpG/TfMjnfPLOUcs8k/zuT+ck/bf58vbVP5E2Z9FBIkqdP72v1V2v8+SWZ9GK7YyaZcvRYR85le81b+qcj48zvzB4zxlg1Y4yLYJyxasYYF8E4YxYCbOe0ooXCp02j2/8nydcleUWSf5bk+umX+X9n8iV+XabND0Wx+804Y9UEPVi1xuf+CUken8nk8rpa64/WWp9fa/2iJP9p+px/m+QnMmlw/1mllEfVW/tz3JLdWGv95SS/mUk/yP/n6L95OPamQY4fTnKPWuuPr+5dsm7OZVwE44xVM8ZYNWOMi2CcsWrGGBfBOGNWAmxzWvFC4X2TfPb0vl9J8plJfiqTRomfn+TRmXzJHldr/b/T54li95BxxqoJenBRDieVmUxKk+Q7aq1vKaVsH46hTCao70nyYbXWd2fS3DdJvnP6Goela9P4mWdObx90+O8c/Xent39VlenrLecyLoJxxqoZY6yaMcZFMM5YNWOMi2CcMS8BtjldwELhA0spozJpwPpXtdanZLJ4+CHT1/v0Wutfru4d0gXGGasm6MFFmU5Ob5vkXUnemOQd04njpcYYuibJbpLbTP/+bZmUWfioUsoXNF+v8TNvnd5e2fh3GBjnMi6CccaqGWOsmjHGRTDOWDVjjItgnDEvAbY5XcBC4RXTL9RB4zmvr7W+ptb6qpW8KTrHOGPVBD24KNNzzeF/90hy/8OJ43TSmkxKH9yc5O2llKunj//L6WPfUUq59nAslVJ2p/dfOb19d+PfYWCcy7gIxhmrZoyxasYYF8E4Y9WMMS6Ccca8BNjmZKGQi2CcsWrGGBdpuqPrhzMpnfBnjYcOx8eHJLldkj+otb5z+jPPyWSH112SfE8m5RlSa90rpXxgkn+dyY6x61f/Dugq5zIugnHGqhljrJoxxkUwzlg1Y4yLYJwxLwG2c7BQyEUwzlg1Y4yLVGv9mVrrdbXWv27cdzjWPnJ6+6tJq4TCtyX5viQfneTnSik/WEr5/kz6Rn50kmclkXU7cM5lXATjjFUzxlg1Y4yLYJyxasYYF8E4Yx47Zz+F49RafybJzxy578SFwmk66LdlEuH+siSPKKU8e/r3D03yyCQ/GAuFNBhnrJoxxkUrk96Plxp/f/8kH5Xk7Ul+K5mUUJiOtzeWUr46yV8keVqSL8lkQvuWJF9da/3Oiz5+usm5jItgnLFqxhirZoxxEYwzVs0Y4yIYZ8xqNB7LRlzECQuFL0pyhyQPrtOmhIdftFLKlUm+NJOFwgfk1oXC77BQyEmMM1bNGOOilVK2aq0HpZTPTvKTSf53kk9PkuZYbDz/zkneN8mdk7yi1vqOizxeNoNzGRfBOGPVjDFWzRjjIhhnrJoxxkUwzjiLANuSWCjkIhhnrJoxxkUrpfx0ks9O8vRa6zMb998zyRdmMq5+qUyaCpu0MBPnMi6CccaqGWOsmjHGRTDOWDVjjItgnHESJSKXpNZ6MP3jpyTZTvKrR6LbrYXCJG+rtb714o+UTWacsWrGGBeplHL3JJ+Y5L1JfnZ63+0zGX9fkOQfZlI+4ZcE15iHcxkXwThj1YwxVs0Y4yIYZ6yaMcZFMM44iQDbElko5CIYZ6yaMcYFeniS22Yylt5WSvn/27lD3KiiMAzDXytYA6khqBt2gEbgUSQQEiwGCwLdBeBwbAGFZQttgjgazQIQZBCMaBgoOTD/f9Ob5zFTMeKItyeT+037MMnTJI+T3EpyPsZ4s+YBubncZXTQGdU0RjWN0UFnVNMYHXTG7xjYjsuDQjrojGoao9SVf/d4lp+fRc6WZTlP8jzJ7SQfkrwcY3xZ75RsgLuMDjqjmsaopjE66IxqGqODzjhwuvYBtmBZlpP9j78+KHyf5FmSj0nu+AXjf+iMahqjy5Vvct3dv95L8jrJ1yQPxhiPjGv8K3cZHXRGNY1RTWN00BnVNEYHnXEdf8F2BH94UHg/yeckT8YYn9Y4F9uiM6ppjBWM/ev3JC/GGO/WPAzb4C6jg86opjGqaYwOOqOaxuigM65jYDsuDwrpoDOqaYwul0leJXk7xvi29mHYHHcZHXRGNY1RTWN00BnVNEYHnXHAwHZcHhTSQWdU0xgtxhgXSS7WPgeb5S6jg86opjGqaYwOOqOaxuigMw6c7Ha7v78LAAAAAAAASJKcrn0AAAAAAAAAuEkMbAAAAAAAADDBwAYAAAAAAAATDGwAAAAAAAAwwcAGAAAAAAAAEwxsAAAAAAAAMMHABgAAAAAAABMMbAAAAAAAADDBwAYAAAAAAAATDGwAAAAAAAAwwcAGAAAAAAAAEwxsAAAAAAAAMMHABgAAAAAAABMMbAAAAAAAADDBwAYAAAAAAAATDGwAAAAAAAAwwcAGAAAAAAAAE34AxVQwx9KqOcUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 289,
       "width": 876
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res.plot_security_weights();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-30</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-29</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-31</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-30</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>631321.251898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>631321.251898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>631321.251898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>631321.251898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>631321.251898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      foo\n",
       "2008-01-30       0.000000\n",
       "2008-01-31       0.000000\n",
       "2008-02-29       0.000000\n",
       "2008-03-31       0.000000\n",
       "2008-04-30       0.000000\n",
       "...                   ...\n",
       "2017-08-31  631321.251898\n",
       "2017-09-30  631321.251898\n",
       "2017-10-31  631321.251898\n",
       "2017-11-30  631321.251898\n",
       "2017-12-31  631321.251898\n",
       "\n",
       "[121 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>example1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>240.302579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>255.046653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31</th>\n",
       "      <td>254.464421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>265.182603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>281.069771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              example1\n",
       "2017-08-31  240.302579\n",
       "2017-09-30  255.046653\n",
       "2017-10-31  254.464421\n",
       "2017-11-30  265.182603\n",
       "2017-12-31  281.069771"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.prices.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>example1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <td>2008-01-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end</th>\n",
       "      <td>2017-12-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_return</th>\n",
       "      <td>1.810698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cagr</th>\n",
       "      <td>0.109805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_drawdown</th>\n",
       "      <td>-0.267046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calmar</th>\n",
       "      <td>0.411186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtd</th>\n",
       "      <td>0.05991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_month</th>\n",
       "      <td>0.102033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six_month</th>\n",
       "      <td>0.22079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ytd</th>\n",
       "      <td>0.879847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_year</th>\n",
       "      <td>0.879847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_year</th>\n",
       "      <td>0.406395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five_year</th>\n",
       "      <td>0.227148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ten_year</th>\n",
       "      <td>0.109805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incep</th>\n",
       "      <td>0.109805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_sharpe</th>\n",
       "      <td>3.299555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_sortino</th>\n",
       "      <td>6.352869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_mean</th>\n",
       "      <td>2.448589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_vol</th>\n",
       "      <td>0.742097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_skew</th>\n",
       "      <td>0.307861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daily_kurt</th>\n",
       "      <td>1.414455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_day</th>\n",
       "      <td>0.137711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst_day</th>\n",
       "      <td>-0.14073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_sharpe</th>\n",
       "      <td>0.723148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_sortino</th>\n",
       "      <td>1.392893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_mean</th>\n",
       "      <td>0.117579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_vol</th>\n",
       "      <td>0.162594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_skew</th>\n",
       "      <td>0.301545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthly_kurt</th>\n",
       "      <td>1.379006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_month</th>\n",
       "      <td>0.137711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst_month</th>\n",
       "      <td>-0.14073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_sharpe</th>\n",
       "      <td>0.503939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_sortino</th>\n",
       "      <td>5.019272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_mean</th>\n",
       "      <td>0.14814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_vol</th>\n",
       "      <td>0.293964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_skew</th>\n",
       "      <td>2.317496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearly_kurt</th>\n",
       "      <td>5.894955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best_year</th>\n",
       "      <td>0.879847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst_year</th>\n",
       "      <td>-0.088543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_drawdown</th>\n",
       "      <td>-0.091255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_drawdown_days</th>\n",
       "      <td>369.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_up_month</th>\n",
       "      <td>0.064341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_down_month</th>\n",
       "      <td>-0.012928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_year_perc</th>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twelve_month_win_perc</th>\n",
       "      <td>0.46789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  example1\n",
       "start                  2008-01-30 00:00:00\n",
       "end                    2017-12-31 00:00:00\n",
       "rf                                     0.0\n",
       "total_return                      1.810698\n",
       "cagr                              0.109805\n",
       "max_drawdown                     -0.267046\n",
       "calmar                            0.411186\n",
       "mtd                                0.05991\n",
       "three_month                       0.102033\n",
       "six_month                          0.22079\n",
       "ytd                               0.879847\n",
       "one_year                          0.879847\n",
       "three_year                        0.406395\n",
       "five_year                         0.227148\n",
       "ten_year                          0.109805\n",
       "incep                             0.109805\n",
       "daily_sharpe                      3.299555\n",
       "daily_sortino                     6.352869\n",
       "daily_mean                        2.448589\n",
       "daily_vol                         0.742097\n",
       "daily_skew                        0.307861\n",
       "daily_kurt                        1.414455\n",
       "best_day                          0.137711\n",
       "worst_day                         -0.14073\n",
       "monthly_sharpe                    0.723148\n",
       "monthly_sortino                   1.392893\n",
       "monthly_mean                      0.117579\n",
       "monthly_vol                       0.162594\n",
       "monthly_skew                      0.301545\n",
       "monthly_kurt                      1.379006\n",
       "best_month                        0.137711\n",
       "worst_month                       -0.14073\n",
       "yearly_sharpe                     0.503939\n",
       "yearly_sortino                    5.019272\n",
       "yearly_mean                        0.14814\n",
       "yearly_vol                        0.293964\n",
       "yearly_skew                       2.317496\n",
       "yearly_kurt                       5.894955\n",
       "best_year                         0.879847\n",
       "worst_year                       -0.088543\n",
       "avg_drawdown                     -0.091255\n",
       "avg_drawdown_days               369.714286\n",
       "avg_up_month                      0.064341\n",
       "avg_down_month                   -0.012928\n",
       "win_year_perc                     0.555556\n",
       "twelve_month_win_perc              0.46789"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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